geointel/docs/CODEX_EXECUTION_LOG.md

Sprint 169 Filtered YOLO candidate gate and operator hardening (2026-07-12)

Changed:

• Trained inactive local model asset geointel-building-yolov8s-aoi1024cleanpx12vis035lowvar512e50-pt from the visually audited AOI1024 dataset after low-variance negative filtering.
• Added SHA256 provenance fields to future training_summary.json output for dataset.yaml, the dataset summary, the local base model and the copied trained model.
• Kept long workbench context names compact with matching native tooltips and clamped readiness values to two lines.
• Archived the training, positive-AOI, split-background and promotion evidence under artifacts/model-review/aoi1024cleanpx12vis035lowvar512e50 locally and matching Tower artifact directories.

Runtime evidence:

• Training completed for 50 CPU epochs with YOLOv8s, image size 512, batch 4; best-model validation ended at precision 0.454, recall 0.491, mAP50 0.368 and mAP50-95 0.149.
• Model catalog SHA256: 75345767b51cc66692a9d2c2cd6577b7feecaf5971c8762365e8b610b9dfde8e; catalog status available, active=false, will_download_models=false.
• Local model-load preflight passed with Torch 2.13.0 and Ultralytics 8.4.92; CUDA is unavailable and no model download occurred.
• Seven-AOI persisted QA matrix produced 28 runs. Best single result was Westerlo at threshold 0.15, F1 0.3002114164904862.
• Mean positive F1 was 0.153943 at threshold 0.05, 0.153872 at 0.15, 0.128891 at 0.25 and 0.099011 at 0.35.
• Strict pure-empty background evidence was clean at thresholds 0.15, 0.25 and 0.35; threshold 0.05 produced one Postel-bos detection.
• Split-aware promotion report recommended none: every threshold failed the positive mean-F1 gate, and 0.05 also failed background false-positive pressure.
• The existing active geointel-building-yolov8s-aoi1024bg512r3e50-pt remains materially stronger at its promoted 0.35 profile with mean F1 0.320866; no model default or .env value was changed.
• Pushed implementation commit 8daaa07 and redeployed the AI-enabled Tower all-in-one container on http://192.168.10.150:1202.
• Post-deploy live migration smoke passed with PostGIS 3.6, required schema objects and Alembic head 202606120900.
• In-app browser verification found no console warnings/errors, confirmed matching full-value context tooltips and a two-line readiness value, and showed both the inactive low-variance candidate and the unchanged active aoi1024bg512r3e50 asset in Detection Lab.

Tested:

• Red step: python -m pytest backend/tests/test_sprint169_long_context_name_readability.py -q failed before context tooltips and readiness clamping were present.
• python -m pytest backend/tests/test_sprint169_long_context_name_readability.py backend/tests/test_sprint22_workbench_status_strip.py backend/tests/test_sprint161_widescreen_workbench.py -q (5 passed).
• Red step: python -m pytest backend/tests/test_sprint129_operator_yolo_training_dataset.py::test_operator_yolo_train_smoke_script_contract -q failed before training hashes were recorded.
• python -m pytest backend/tests/test_sprint129_operator_yolo_training_dataset.py -q (3 passed).
• bash -n scripts/train_operator_yolo_detector.sh and frontend typecheck passed.
• Full readiness: bash scripts/run_readiness_check.sh (461 passed; one Alembic head; frontend typecheck/build and shell syntax gates passed).

Next:

• Do not retrain the same architecture blindly. Inspect per-AOI false-negative evidence and improve label geometry/class balance or add targeted positive samples for the weakest AOIs before the next candidate.

Sprint 164 Tower AI deploy env hardening (2026-07-11)

Changed:

• Hardened scripts/deploy_tower.sh and scripts/deploy_tower.ps1 so the remote Tower .env is sourced before building the all-in-one image.
• Updated the PowerShell deploy wrapper to stream the remote script through bash -s, matching the Bash deploy path and preserving Bash variable expansion during .env-driven builds.
• The PowerShell wrapper now writes a UTF-8-without-BOM temporary script, copies it with scp, runs it with bash on Tower and removes the remote temp file while preserving the deploy exit code.
• GEOINTEL_INSTALL_AI=true in /mnt/user/appdata/geointel/.env now drives the automatic image build by default; explicit local overrides remain possible for one-off deploys.
• Documented the deploy behavior in deploy/unraid/README.md.

Why:

• A manual docker compose up -d --build against the multi-container compose file failed on Tower because Docker had exhausted default bridge address pools. The healthy runtime is the Unraid all-in-one container, which should be redeployed through the Dockerman-native scripts instead.
• The previous deploy script path could build a GIS-only image while the remote runtime .env enabled YOLO, leaving the configured detector in dependency_unavailable.

Tested:

• Red step: python -m pytest backend/tests/test_sprint31_unraid_template.py::test_tower_deploy_build_uses_remote_env_ai_setting_by_default failed because the deploy scripts did not source remote .env before docker build.
• python -m pytest backend/tests/test_sprint31_unraid_template.py::test_tower_deploy_build_uses_remote_env_ai_setting_by_default backend/tests/test_sprint31_unraid_template.py::test_tower_deploy_uses_single_container_unraid_compose backend/tests/test_docker_runtime_config.py::test_unraid_deploy_passes_ai_build_arg_and_yolo_runtime_env (3 passed).
• Red step: python -m pytest backend/tests/test_sprint31_unraid_template.py::test_powershell_tower_deploy_streams_remote_script_to_bash -q failed because the PowerShell wrapper passed the remote script as an SSH command argument instead of streaming it to bash -s.
• python -m pytest backend/tests/test_sprint31_unraid_template.py::test_powershell_tower_deploy_streams_remote_script_to_bash backend/tests/test_sprint31_unraid_template.py::test_tower_deploy_build_uses_remote_env_ai_setting_by_default backend/tests/test_docker_runtime_config.py::test_unraid_deploy_passes_ai_build_arg_and_yolo_runtime_env -q (3 passed).
• Red step: the same PowerShell deploy transport test failed until the wrapper wrote a [System.Text.UTF8Encoding]::new($false) temp script, copied it via scp, ran it through remote bash and propagated the remote exit code.

Sprint 150 YOLO label visible-ratio gate (2026-07-09)

Changed:

• Added --min-label-visible-ratio / OPERATOR_YOLO_MIN_LABEL_VISIBLE_RATIO to scripts/export_operator_yolo_tile_dataset.py.
• The tile exporter now computes the visible share of each original building bbox inside a tile and can drop labels below the configured ratio.
• Default remains 0 for legacy behavior; use 0.25 for the next overlap-heavy operator dataset experiment.
• Tile dataset summaries include min_label_visible_ratio.
• scripts/audit_operator_yolo_dataset_quality.py now reports min_label_visible_ratio in JSON and Markdown.
• Added operator-only --width, --height and --half-size-scale options to scripts/prepare_operator_real_data_samples.py; generated raster names now include the requested width.
• Updated operator script documentation.

Why:

• The current rejected AOI512 candidate still shows low precision/recall after max-det and duplicate suppression hardening.
• A likely label-quality issue is that overlapping tile export can create many small clipped edge labels for buildings mostly outside a tile.
• This pass improves the next training dataset gate without activating a model, faking detections, fetching providers or changing persistence.

Tested:

• Red step: python -m pytest backend\tests\test_sprint130_operator_yolo_tile_dataset.py -q failed because the exporter lacked min_label_visible_ratio, CLI help and visible-fragment filtering.
• python -m pytest backend\tests\test_sprint130_operator_yolo_tile_dataset.py -q (6 passed)
• Red step: python -m pytest backend\tests\test_sprint146_operator_yolo_dataset_quality_audit.py -q failed because the audit report did not expose min_label_visible_ratio.
• python -m pytest backend\tests\test_sprint130_operator_yolo_tile_dataset.py backend\tests\test_sprint146_operator_yolo_dataset_quality_audit.py -q (7 passed)
• Red step: python -m pytest backend\tests\test_sprint127_operator_sample_quality_matrix.py backend\tests\test_sprint131_operator_sample_expansion.py -q failed because sample prep lacked larger-AOI options.
• python -m pytest backend\tests\test_sprint127_operator_sample_quality_matrix.py backend\tests\test_sprint131_operator_sample_expansion.py -q (8 passed)
• Full readiness: bash scripts/run_readiness_check.sh (427 passed, frontend typecheck/build passed).
• Tower deploy: first AI rebuild failed with Docker storage full; after Docker build cache cleanup /var/lib/docker had 98G free and redeploy passed live migration smoke and browser runtime verification on http://192.168.10.150:1202.
• Tower dataset audit: exported /app/storage/operator-data/yolo-building-aoi512-visible025 with min_label_visible_ratio=0.25; audit returned needs_attention because the current 512x512 source rasters still produce only 16 tiles and median normalized box area remains below gate.

Next:

• Prepare a larger explicit operator sample manifest, for example /app/storage/operator-data/operator-samples-1024 with --width 1024 --height 1024 --half-size-scale 2, then export/audit yolo-building-aoi1024-visible025 before another CPU training candidate.

Sprint 149 YOLO duplicate suppression evidence (2026-07-09)

Changed:

• Added configured-YOLO cross-tile duplicate suppression after pixel boxes are converted to EPSG:4326 geometries and before Detection rows are persisted.
• Added backend setting YOLO_DUPLICATE_IOU_THRESHOLD with default 0.5; 0 disables the GeoIntel-side pass for debugging.
• Detection run result_json now records:
  • raw_detection_count
  • suppressed_detection_count
  • duplicate_iou_threshold
• Updated .env.example, Docker Compose, Unraid env examples and the Dockerman run script.
• Updated calibration and quality matrix scripts to fetch detection run details and include raw/suppressed counts in per-run and aggregate summaries.
• Updated backend/API/AI/pipeline documentation.

Why:

• Dense overlapping tile inference can produce duplicate candidate buildings, which inflates persisted false positives before QA/QC.
• The previous Sprint 148 cap fix allowed dense AOIs to persist more candidates, but made duplicate pressure more visible.
• This pass keeps all outputs honest: no model activation, no fake detections and no migration. It only removes lower-confidence same-class geometric duplicates before persistence.

Tested:

• Red step: python -m pytest backend\tests\test_sprint8b_yolo_foundation.py::test_yolo_run_suppresses_cross_tile_duplicate_detections -q failed with detection_count == 2.
• python -m pytest backend\tests\test_sprint8b_yolo_foundation.py::test_yolo_run_suppresses_cross_tile_duplicate_detections -q (1 passed)
• Red step: Docker runtime config tests failed before .env.example and Unraid runner exposed YOLO_DUPLICATE_IOU_THRESHOLD.
• python -m pytest backend\tests\test_sprint8b_yolo_foundation.py backend\tests\test_docker_runtime_config.py::test_env_example_uses_runtime_env_names_read_by_backend_and_frontend backend\tests\test_docker_runtime_config.py::test_unraid_deploy_passes_ai_build_arg_and_yolo_runtime_env -q (17 passed)
• Red step: calibration/matrix script tests failed before detection-run raw/suppressed metadata was included.
• python -m pytest backend\tests\test_sprint124_detection_calibration_sweep.py backend\tests\test_sprint126_detection_quality_matrix.py backend\tests\test_sprint127_operator_sample_quality_matrix.py::test_multi_sample_detection_quality_matrix_runs_existing_matrix_for_each_sample -q (3 passed)
• bash -n scripts/run_detection_calibration_sweep.sh
• bash -n scripts/run_detection_quality_matrix.sh
• bash -n scripts/run_multi_sample_detection_quality_matrix.sh
• bash scripts/run_readiness_check.sh (426 passed, frontend typecheck/build passed)
• Redeployed Tower all-in-one image with AI dependencies and verified YOLO_MAX_DETECTIONS=1000 plus YOLO_DUPLICATE_IOU_THRESHOLD=0.5 in the live container.
• Tower live migration smoke and browser runtime verification passed.
• Live Westerlo calibration with geointel-building-yolov8s-aoi512e80-pt:
  • 0.25: 202 persisted / 270 raw / 68 suppressed, F1 0.2537313432835821
  • 0.15: 328 persisted / 523 raw / 195 suppressed, F1 0.22054380664652568
  • 0.05: 586 persisted / 1000 raw / 414 suppressed, F1 0.17173913043478262
• Live Turnhout calibration with geointel-building-yolov8s-aoi512e80-pt:
  • 0.25: 559 persisted / 822 raw / 263 suppressed, F1 0.14114114114114112
  • 0.15: 659 persisted / 1000 raw / 341 suppressed, F1 0.14385474860335196
  • 0.05: 659 persisted / 1000 raw / 341 suppressed, F1 0.14385474860335196

Conclusion:

• Cross-tile duplicate suppression reduces false-positive pressure and improves F1 versus the Sprint 148 uncapped baseline on the checked dense AOIs.
• The current AOI512 YOLOv8s candidate remains rejected for default use because recall/precision are still too low after post-processing.
• Next model work should focus on stronger training data/label strategy and a new candidate gate, not further default threshold lowering.

Sprint 148 YOLO max-detection cap hardening (2026-07-09)

Changed:

• Added backend setting YOLO_MAX_DETECTIONS / Settings.yolo_max_detections.
• YoloDetectionAdapter now forwards the value to Ultralytics as max_det.
• Default is 1000 instead of relying on Ultralytics' upstream default of 300.
• Added Docker/Unraid/runtime wiring:
  • .env.example
  • docker-compose.yml
  • docker-compose.unraid.yml
  • deploy/unraid/geointel.env.example
  • deploy/unraid/run-dockerman-container.sh
• Updated backend/API/AI environment documentation.

Why:

• Real Kempen building AOIs often contain more than 300 reference buildings.
• The previous configured-YOLO path could saturate at 300 detections before QA/QC, capping recall independently of model quality.
• This does not activate a model and does not fake detections; it removes an inference runtime cap so persisted QA/QC can measure candidate models honestly.

Tested:

• Red step: targeted YOLO adapter tests failed because max_det was not passed to the model.
• python -m pytest backend\tests\test_sprint8b_yolo_foundation.py::test_yolo_adapter_converts_single_band_tiles_to_rgb_before_prediction backend\tests\test_sprint8b_yolo_foundation.py::test_yolo_adapter_uses_configured_max_detections -q (2 passed)
• Red step: Docker runtime config tests failed before .env.example and Unraid runner exposed YOLO_MAX_DETECTIONS.
• python -m pytest backend\tests\test_docker_runtime_config.py::test_env_example_uses_runtime_env_names_read_by_backend_and_frontend backend\tests\test_docker_runtime_config.py::test_unraid_deploy_passes_ai_build_arg_and_yolo_runtime_env backend\tests\test_sprint8b_yolo_foundation.py -q (16 passed)
• bash scripts/run_readiness_check.sh (425 passed, frontend typecheck/build passed)
• Redeployed Tower all-in-one image with AI dependencies and verified YOLO_MAX_DETECTIONS=1000 in the live container.
• Tower live migration smoke passed against embedded PostGIS.
• Live Westerlo calibration with geointel-building-yolov8s-aoi512e80-pt:
  • 0.25: 270 detections, F1 0.23509933774834438
  • 0.15: 523 detections, F1 0.19603267211201864
  • 0.05: 1000 detections, F1 0.13193403298350823
• Live Turnhout calibration with geointel-building-yolov8s-aoi512e80-pt:
  • 0.25: 822 detections, F1 0.1304075235109718
  • 0.15: 1000 detections, F1 0.13085166384658772
  • 0.05: 1000 detections, F1 0.13085166384658772

Conclusion:

• The former 300-detection runtime cap is removed; dense AOIs can now persist more candidates.
• The current AOI512 YOLOv8s candidate remains rejected for operational use because precision/recall quality is still too low and low thresholds saturate the configured 1000 cap.
• Next model work should focus on training data coverage, label strategy and post-processing/NMS behavior rather than only threshold lowering.

Sprint 147 AOI512 YOLOv8s scale-match candidate gate (2026-07-09)

Changed:

• Built an alternate operator YOLO dataset at AOI scale instead of small 160px tiles:
  • output /app/storage/operator-data/yolo-building-aoi512-uniquehardneg
  • tile size 512, stride 512
  • validation samples turnhout, retie, westerlo, arendonk_heide
  • 16 total images, 13 positive images, 3 negative images, 3172 labels
• Audited the AOI512 dataset:
  • output /mnt/user/appdata/geointel/artifacts/operator-yolo-dataset-audits/aoi512-uniquehardneg/operator_yolo_dataset_quality_audit.json
  • status ok
  • no missing label files
  • no invalid label rows
  • 13 positive samples
  • 3 positive validation samples
  • median normalized box area 0.000793456875
• Trained a Tower-local YOLOv8s scale-match candidate:
  • dataset /app/storage/operator-data/yolo-building-aoi512-uniquehardneg/dataset.yaml
  • base model /app/models/yolov8s.pt
  • 80 CPU epochs
  • image size 512
  • batch 4
  • artifact /app/models/geointel-building-yolov8s-aoi512e80.pt
  • model asset id geointel-building-yolov8s-aoi512e80-pt
  • SHA256 b796284a13358498c296fa41270dd234a81718a76cdafe62490a5206bb86ac5b
• The training validation improved versus the previous 160px candidate but remained weak:
  • precision 0.404
  • recall 0.270
  • mAP50 0.163
  • mAP50-95 0.0505

Tested:

• Ran 7-AOI positive calibration sweeps for Geel, Mol, Turnhout, Herentals, Balen, Retie and Westerlo:
  • output root /mnt/user/appdata/geointel/artifacts/detection-calibration/aoi512e80-positive
  • best sample: Westerlo threshold 0.25, F1 0.23509933774834438, precision 0.26296296296296295, recall 0.2125748502994012, detections 270
  • most other AOIs remained around F1 0.10 to 0.14
• Ran 9-sample hard-negative/background matrix:
  • output /mnt/user/appdata/geointel/artifacts/detection-hard-negatives/aoi512e80/hard_negative_matrix_summary.json
  • threshold 0.25: total background detections 105, max sample detections 56
  • threshold 0.15: total background detections 181, max sample detections 100
  • threshold 0.05: total background detections 503, max sample detections 278
• Assembled positive evidence portfolio:
  • output /mnt/user/appdata/geointel/artifacts/detection-calibration-portfolio/aoi512e80-positive/calibration_evidence_portfolio.json
  • sample count 7
  • evidence features 17156
• Ran promotion report:
  • output /mnt/user/appdata/geointel/artifacts/detection-model-promotion/aoi512e80-positive-vs-hard-negative/detection_model_promotion_report.json
  • threshold 0.05: rejected for positive_mean_f1_below_gate and background_false_positive_pressure, mean F1 0.1335249717919908, max background detections 278
  • threshold 0.15: rejected for the same reasons, mean F1 0.1335249717919908, max background detections 100
  • threshold 0.25: rejected for the same reasons, mean F1 0.13511851520077328, max background detections 56
  • recommended candidate none

Open:

• Do not activate geointel-building-yolov8s-aoi512e80-pt as the V1 default.
• AOI-scale training improves the Ultralytics validation curve but does not improve persisted QA/QC enough for operational use.
• The recurring failure mode is overproduction near the 300-detection cap with low recall and too many false positives.
• Next recommended pass: add/export more diverse positive AOIs and improve label quality/geometry-to-box strategy before training another higher-capacity model. A pure scale or epoch change is not enough.

Sprint 146 Unique hard-negative YOLOv8s candidate gate (2026-07-09)

Changed:

• Fixed the all-in-one Docker runtime so /app/scripts/train_operator_yolo_detector.sh is copied into the image and made executable.
• Added regression coverage in backend/tests/test_docker_runtime_config.py so the all-in-one image must include the operator YOLO training wrapper.
• Hardened scripts/build_detection_model_promotion_report.py with explicit --default-positive-tile-size and --default-positive-tile-overlap options for older positive evidence portfolios that record the model at portfolio level but omit per-run tile provenance.
• Added regression coverage in backend/tests/test_sprint143_detection_model_promotion_report.py for portfolio-level model fallback plus explicit positive tile defaults.

Tested:

• Red step: python -m pytest backend\tests\test_docker_runtime_config.py::test_all_in_one_dockerfile_copies_operator_scripts_for_runtime_use -q failed because the all-in-one Dockerfile did not copy the training wrapper.
• python -m pytest backend\tests\test_docker_runtime_config.py -q (22 passed)
• bash scripts/run_readiness_check.sh (423 passed; frontend typecheck/build passed; Alembic head 202606120900)
• Pushed commit afd2cba and redeployed Tower with .\scripts\deploy_tower.ps1 -InstallAi true; browser runtime verification and live migration smoke passed.
• Verified the wrapper in the live container: /app/scripts/train_operator_yolo_detector.sh exists and is executable.
• Trained geointel-building-yolov8s-uniquehardneg160e50-pt on Tower from /app/storage/operator-data/yolo-building-tile-uniquehardneg160/dataset.yaml using local /app/models/yolov8s.pt, 50 CPU epochs, image size 160, batch 8.
• Training completed with final validation precision 0.38, recall 0.365, mAP50 0.278, mAP50-95 0.0803.
• Live model asset:
  • /app/models/geointel-building-yolov8s-uniquehardneg160e50.pt
  • model asset id geointel-building-yolov8s-uniquehardneg160e50-pt
  • SHA256 c4e480273d3da5fc27532cd8bdc3fa7786582e06848ea11b56714ab8cb1750b3
• Ran 7-AOI positive calibration sweeps for Geel, Mol, Turnhout, Herentals, Balen, Retie and Westerlo:
  • output root /mnt/user/appdata/geointel/artifacts/detection-calibration/uniquehardneg160e50-positive
  • best observed AOI result: Westerlo threshold 0.25, F1 0.384180790960452, precision 0.5177664974619289, recall 0.30538922155688625, detections 197
  • other positive AOIs remained weak, with best F1 roughly 0.10 to 0.17.
• Assembled positive evidence portfolio:
  • output /mnt/user/appdata/geointel/artifacts/detection-calibration-portfolio/uniquehardneg160e50-positive/calibration_evidence_portfolio.json
  • sample count 7
  • evidence features 17008
  • role counts false_negative=9710, false_positive=4734, match_candidate=1282, match_reference=1282
• Ran 9-sample hard-negative/background matrix:
  • output /mnt/user/appdata/geointel/artifacts/detection-hard-negatives/uniquehardneg160e50/hard_negative_matrix_summary.json
  • threshold 0.25: total background detections 98, max sample detections 58
  • threshold 0.15: total background detections 137, max sample detections 85
  • threshold 0.05: total background detections 276, max sample detections 172
• Red step: python -m pytest backend\tests\test_sprint143_detection_model_promotion_report.py -q failed because the promotion report could not yet accept explicit positive tile defaults.
• python -m pytest backend\tests\test_sprint143_detection_model_promotion_report.py -q (2 passed)
• Rebuilt the promotion report using explicit positive tile defaults:
  • output /mnt/user/appdata/geointel/artifacts/detection-model-promotion/uniquehardneg160e50-positive-vs-hard-negative-v2/detection_model_promotion_report.json
  • threshold 0.05: rejected for positive_mean_f1_below_gate and background_false_positive_pressure, mean F1 0.15797188547918842, max background detections 172
  • threshold 0.15: rejected for the same reasons, mean F1 0.1598974047548654, max background detections 85
  • threshold 0.25: rejected for the same reasons, mean F1 0.15967766715169612, max background detections 58
  • recommended candidate none

Open:

• Do not activate geointel-building-yolov8s-uniquehardneg160e50-pt as the V1 default.
• The candidate improves some individual AOIs but still combines low mean positive F1 with unacceptable false-positive pressure on background samples.
• Next model work should focus on materially better training data/model strategy, not another default activation attempt with this artifact.

Sprint 145 YOLOv8s hardneg r8 e60 full candidate evaluation (2026-07-08)

Changed:

• Verified that the Tower-local YOLOv8s hardneg r8 training run completed all 60 requested CPU epochs.
• Finalized the completed model artifact:
  • /app/models/geointel-building-yolov8s-hardneg160r8e60.pt
  • model asset id geointel-building-yolov8s-hardneg160r8e60-pt
  • SHA256 d7daea04bd51a54a06944f0d4bf1961fe453dceb1fb34ef51daa73f6901fca81
• Wrote /app/storage/training/operator-yolo/geointel-building-yolov8s-hardneg160r8e60/training_summary.json.

Tested:

• Training summary reports status=ok, requested epochs 60, completed epochs 60.
• Ran 7-AOI positive matrix:
  • output /mnt/user/appdata/geointel/artifacts/detection-quality-matrix/multi-sample/yolov8s-hardneg160r8e60-positive-20260708/multi_sample_quality_summary.json
  • sample count 7
  • run count 21
  • best result: Westerlo threshold 0.15, F1/score 0.20588235294117646, precision 0.26666666666666666, recall 0.16766467065868262, detections 210, false positives 154, false negatives 278.
• Ran hard-negative matrix:
  • output /mnt/user/appdata/geointel/artifacts/detection-hard-negatives/yolov8s-hardneg160r8e60-live/hard_negative_matrix_summary.json
  • Postel-bos detections 0/0/0 at thresholds 0.05/0.15/0.25
  • Lommel-heide detections 0/0/0
  • Kasterlee-bos detections 30/11/9
• Assembled evidence portfolio:
  • output /mnt/user/appdata/geointel/artifacts/detection-calibration-portfolio/yolov8s-hardneg160r8e60-positive-20260708/output/calibration_evidence_portfolio.json
  • sample count 7
  • evidence features 15714
  • role counts false_negative=10468, false_positive=4198, match_candidate=524, match_reference=524
• Ran promotion report:
  • output /mnt/user/appdata/geointel/artifacts/detection-model-promotion/yolov8s-hardneg160r8e60-20260708/detection_model_promotion_report.json
  • evaluated 3 candidate thresholds
  • recommended candidate none
  • threshold 0.05: mean F1 0.07825931710275633, max background detections 30, rejected for positive_mean_f1_below_gate and background_false_positive_pressure
  • threshold 0.15: mean F1 0.07870592446136859, max background detections 11, rejected for the same reasons
  • threshold 0.25: mean F1 0.06111741186503092, max background detections 9, rejected for the same reasons
• Comparison baseline: geointel-building-yolov8n-expanded160e50-pt|640|64|0.15 had mean F1 0.19718007234060198 and max background detections 46.

Open:

• The e60 YOLOv8s hardneg r8 candidate improves hard-negative behavior at threshold 0.15 compared with expanded160e50, but its positive-AOI mean F1 is too low for operational use.
• Do not activate this model as default.
• Next model pass should focus on improving positive recall/fit without losing the hard-negative gains, likely through better labels, more positive AOIs, or a different architecture/training strategy rather than simply extending this same run.

Sprint 144 YOLOv8s hardneg r8 partial candidate evaluation (2026-07-08)

Changed:

• Started a Tower-local YOLOv8s training run using:
  • dataset /app/storage/operator-data/yolo-building-tile-hardneg160r8/dataset.yaml
  • base model /app/models/yolov8s.pt
  • requested epochs 60
  • image size 640
  • batch 2
  • device cpu
• The Codex command reached its 1-hour timeout after 12 completed epochs; the run had produced weights/best.pt and weights/last.pt.
• Preserved the partial best artifact as /app/models/geointel-building-yolov8s-hardneg160r8e12partial.pt.
• Wrote /app/storage/training/operator-yolo/geointel-building-yolov8s-hardneg160r8e60/training_summary_partial_e12.json.
• Treated the artifact explicitly as a partial evaluation candidate, not as a completed 60-epoch model.

Tested:

• Live model catalog listed geointel-building-yolov8s-hardneg160r8e12partial-pt with SHA256 0246202cddc47eb994a0afc9ee10d56b72298bd1cdc0b72b75e12b28e2202330.
• Ran 7-AOI positive matrix:
  • output /mnt/user/appdata/geointel/artifacts/detection-quality-matrix/multi-sample/yolov8s-hardneg160r8e12partial-positive-20260708/multi_sample_quality_summary.json
  • sample count 7
  • run count 21
  • best result: Westerlo threshold 0.05, F1/score 0.14826498422712936, precision 0.15666666666666668, recall 0.1407185628742515, detections 300, false positives 253, false negatives 287.
• Ran hard-negative matrix:
  • output /mnt/user/appdata/geointel/artifacts/detection-hard-negatives/yolov8s-hardneg160r8e12partial-live/hard_negative_matrix_summary.json
  • Postel-bos detections 0/0/0 at thresholds 0.05/0.15/0.25
  • Lommel-heide detections 0/0/0
  • Kasterlee-bos detections 18/1/0
• Assembled evidence portfolio:
  • output /mnt/user/appdata/geointel/artifacts/detection-calibration-portfolio/yolov8s-hardneg160r8e12partial-positive-20260708/output/calibration_evidence_portfolio.json
  • sample count 7
  • evidence features 12908
  • role counts false_negative=10861, false_positive=1785, match_candidate=131, match_reference=131
• Ran promotion report:
  • output /mnt/user/appdata/geointel/artifacts/detection-model-promotion/yolov8s-hardneg160r8e12partial-20260708/detection_model_promotion_report.json
  • evaluated 3 candidate thresholds
  • recommended candidate none
  • threshold 0.05 rejected for positive_mean_f1_below_gate and background_false_positive_pressure, with mean F1 0.05026994383963278 and max background detections 18
  • threshold 0.15 rejected for the same reasons, with mean F1 0.0022606965174129354 and max background detections 1
  • threshold 0.25 rejected for insufficient positive evidence and positive F1 below gate.

Open:

• The partial YOLOv8s r8 candidate is materially worse than the existing expanded160e50 positive-AOI baseline and must not be activated.
• CPU-only training is too slow for a complete 60-epoch YOLOv8s pass inside a 1-hour interactive command window.
• Next pass should either resume/finish long-running training outside the interactive timeout or use GPU/accelerated runtime; only then rerun the same positive, hard-negative, evidence portfolio and promotion gates.

Sprint 143 Detection model promotion decision report (2026-07-08)

Changed:

• Added scripts/build_detection_model_promotion_report.py as operator-only evidence tooling.
• The script combines a positive-AOI calibration_evidence_portfolio.json with one or more hard_negative_matrix_summary.json files.
• Candidate rows are grouped by model_asset_id, tile_size, tile_overlap and threshold.
• Promotion gates are explicit:
  • minimum positive sample count
  • minimum background sample count
  • minimum mean positive F1
  • maximum background detections per sample
• Added readiness py_compile coverage for the new script.
• Documented the Tower command in scripts/README.md.

Tested:

• Red step: python -m pytest backend\tests\test_sprint143_detection_model_promotion_report.py -q failed because the report script did not exist.
• python -m pytest backend\tests\test_sprint143_detection_model_promotion_report.py -q (1 passed)
• python -m pytest backend\tests\test_sprint143_detection_model_promotion_report.py backend\tests\test_sprint139_multi_aoi_calibration_evidence_portfolio.py -q (2 passed)
• python -m py_compile scripts\build_detection_model_promotion_report.py
• python scripts\smoke_docs.py
• bash scripts/run_readiness_check.sh (419 passed; frontend typecheck/build passed; Alembic head 202606120900)
• Tower pulled commit c2fba67 and generated:
  • /mnt/user/appdata/geointel/artifacts/detection-model-promotion/positive-expanded-vs-hard-negative-20260708/detection_model_promotion_report.json
  • /mnt/user/appdata/geointel/artifacts/detection-model-promotion/positive-expanded-vs-hard-negative-20260708/detection_model_promotion_report.md
• Live promotion report evaluated 15 model/tile/threshold candidates and recommended none.
• Best current positive candidate geointel-building-yolov8n-expanded160e50-pt|640|64|0.15 was rejected by gates:
  • positive_mean_f1_below_gate
  • background_false_positive_pressure
  • positive samples 7
  • background samples 3
  • mean F1 0.19718007234060198
  • max background detections 46
  • total background detections 46
• Tower container remained healthy on 0.0.0.0:1202->80/tcp.

Open:

• No evaluated model/threshold is ready for default promotion.
• Next model work should improve positive recall/F1 while preserving a strict hard-negative false-positive gate.

Sprint 141 Expanded positive-AOI matrix and portfolio metadata hardening (2026-07-08)

Changed:

• Ran a fresh Tower multi-sample quality matrix for additional positive AOIs balen, herentals and westerlo using:
  • geointel-building-yolov8n-expanded160e50-pt
  • geointel-building-yolov8n-hardneg160r8e40-pt
  • tile size 640, overlap 64, thresholds 0.15 and 0.05.
• Assembled a broader positive-AOI evidence portfolio across 7 AOIs:
  • Geel, Mol, Turnhout and Retie from expanded160e50-live.
  • Balen, Herentals and Westerlo from the fresh balen-herentals-westerlo-live-20260708 run.
• Hardened scripts/export_detection_calibration_evidence.sh so evidence bundles preserve calibration provenance fields in run summaries and GeoJSON properties:
  • model_asset_id
  • model_request
  • tile_size
  • tile_overlap
• Extended backend/tests/test_sprint139_multi_aoi_calibration_evidence_portfolio.py to assert portfolio best-run model/tile provenance is retained.

Tested:

• Red step: python -m pytest backend\tests\test_sprint139_multi_aoi_calibration_evidence_portfolio.py -q failed with KeyError: 'model_asset_id' while bundle summaries dropped model provenance.
• python -m pytest backend\tests\test_sprint139_multi_aoi_calibration_evidence_portfolio.py -q (1 passed)
• python -m pytest backend\tests\test_sprint139_multi_aoi_calibration_evidence_portfolio.py backend\tests\test_sprint138_calibration_evidence_bundle_smoke.py backend\tests\test_sprint137_browser_calibration_summary_evidence_script.py backend\tests\test_sprint125_detection_calibration_evidence_bundle.py -q (4 passed)
• bash -n scripts/export_detection_calibration_evidence.sh
• bash -n scripts/assemble_detection_calibration_evidence_portfolio.sh
• python -m compileall backend/app
• bash scripts/run_readiness_check.sh (418 passed; frontend typecheck/build passed; Alembic head 202606120900; shell syntax gates passed)
• Tower fresh matrix completed for Balen, Herentals and Westerlo.
• Tower positive-AOI portfolio assembly completed for 7 AOIs.
• Tower runtime remained healthy on 0.0.0.0:1202->80/tcp.

Evidence:

• Fresh Balen/Herentals/Westerlo output: /mnt/user/appdata/geointel/artifacts/detection-quality-matrix/multi-sample/balen-herentals-westerlo-live-20260708/multi_sample_quality_summary.json.
• Expanded 7-AOI portfolio output: /mnt/user/appdata/geointel/artifacts/detection-calibration-portfolio/positive-aoi-expanded-20260708/output/calibration_evidence_portfolio.json.
• Expanded positive-AOI portfolio sample count: 7.
• Expanded positive-AOI evidence features: 12438.
• Expanded positive-AOI role counts: false_negative=9200, false_positive=2270, match_candidate=484, match_reference=484.
• Best fresh positive-AOI result: Westerlo with geointel-building-yolov8n-expanded160e50-pt, threshold 0.05, F1/score 0.3659305993690852, precision 0.38666666666666666, recall 0.3473053892215569.
• Fresh Balen best: expanded160e50 threshold 0.05, F1/score 0.16091954022988506, precision 0.16333333333333333, recall 0.15857605177993528.
• Fresh Herentals best: expanded160e50 threshold 0.05, F1/score 0.14093264248704665, precision 0.22666666666666666, recall 0.10225563909774436.
• Hard-negative context from existing matrices:
  • expanded160e50 at 0.05: Kasterlee-bos 76, Lommel-heide 10, Postel-bos 1 detections.
  • hardneg160r8e40 at 0.05: Kasterlee-bos 25, Lommel-heide 0, Postel-bos 0 detections.

Open:

• expanded160e50 is stronger on positive AOIs, especially Westerlo, but still produces many false positives and misses many references.
• hardneg160r8e40 is cleaner on hard-negative AOIs but materially weaker on the fresh positive AOIs.
• No current candidate should be promoted blindly as V1 default without a combined positive/hard-negative decision rule.

Limitations:

• This pass ran additional live inference/QA workflows and produced operator artifacts, but did not change backend APIs, migrations, frontend runtime behavior, model weights, provider fetching or Docker runtime configuration.
• The 7-AOI portfolio initially generated before the metadata fix lacked model/tile provenance in best_run_by_score; it should be regenerated after Tower pulls this commit.

Next recommended pass:

• Add a model promotion decision report that combines positive-AOI F1/recall and hard-negative false-positive pressure into one explicit accept/reject table per model and threshold.

Sprint 140 Live multi-AOI calibration portfolio run (2026-07-08)

Changed:

• Created a Tower-local calibration-evidence-portfolio-manifest.json for existing persisted multi-sample quality matrix summaries:
  • Geel: /mnt/user/appdata/geointel/artifacts/detection-quality-matrix/multi-sample/20260707T025932Z/geel/quality_matrix_summary.json
  • Mol: /mnt/user/appdata/geointel/artifacts/detection-quality-matrix/multi-sample/20260707T025932Z/mol/quality_matrix_summary.json
  • Turnhout: /mnt/user/appdata/geointel/artifacts/detection-quality-matrix/multi-sample/20260707T025932Z/turnhout/quality_matrix_summary.json
• Ran scripts/assemble_detection_calibration_evidence_portfolio.sh against the live Tower app at http://127.0.0.1:1202.
• Produced the first real multi-AOI calibration evidence handoff:
  • /mnt/user/appdata/geointel/artifacts/detection-calibration-portfolio/live-20260708/output/calibration_evidence_portfolio.json
  • /mnt/user/appdata/geointel/artifacts/detection-calibration-portfolio/live-20260708/output/calibration_evidence_portfolio.md
  • per-sample calibration_evidence.geojson, calibration_evidence_summary.json and calibration_evidence_review.html artifacts for Geel, Mol and Turnhout.

Tested:

• Tower manifest validation passed with python3 -m json.tool.
• Live portfolio assembly passed against persisted QA evidence from the running app.
• Output validation passed: portfolio JSON/Markdown and all per-sample evidence GeoJSON/summary/review files exist and are non-empty.
• Tower runtime remained healthy on 0.0.0.0:1202->80/tcp.

Evidence:

• Portfolio sample count: 3.
• Total evidence features: 5509.
• Combined role counts: false_negative=5239, false_positive=164, match_candidate=53, match_reference=53.
• Best sample by score: mol.
• Best per-sample scores:
  • Geel: threshold 0.15, F1/score 0.0187207488299532, precision 0.25, recall 0.009724473257698542, evidence features 1885.
  • Mol: threshold 0.15, F1/score 0.04195804195804196, precision 0.16363636363636364, recall 0.02406417112299465, evidence features 1223.
  • Turnhout: threshold 0.15, F1/score 0.03597122302158273, precision 0.2459016393442623, recall 0.019404915912031046, evidence features 2401.

Open:

• The live portfolio confirms the evidence pipeline works, but the evaluated model/threshold set is not yet operational-quality for building extraction due to very low recall and a dominant false-negative count.

Limitations:

• This pass produced operator artifacts only. It did not rerun inference, mutate application data, change database state, add endpoints, change frontend runtime behavior, download models, train models or rebuild the Docker app.
• The portfolio input came from the existing 20260707T025932Z quality matrix; the next model review should use fresh matrices when new AOIs or model candidates are added.

Next recommended pass:

• Add more positive and hard-negative AOIs, then run a fresh multi-sample quality matrix and portfolio for the next model candidate. Do not promote the current evaluated model as a V1 default.

Sprint 139 Multi-AOI calibration evidence portfolio (2026-07-08)

Changed:

• Added scripts/assemble_detection_calibration_evidence_portfolio.sh for packaging multiple AOI calibration summaries and their persisted QA evidence bundles into one model-review portfolio.
• The assembler reads calibration-evidence-portfolio-manifest.json, copies each AOI summary into a sample folder, runs the existing scripts/export_detection_calibration_evidence.sh exporter per sample and writes:
  • calibration_evidence_portfolio.json
  • calibration_evidence_portfolio.md
• Added optional CURL_BIN support to scripts/export_detection_calibration_evidence.sh so operator smokes/tests can inject a deterministic endpoint mock while defaulting to normal curl.
• Added readiness syntax coverage and operator docs for the manifest convention.
• Updated scripts/README.md, CHANGELOG.md and docs/TODO.md.
• Added regression coverage in backend/tests/test_sprint139_multi_aoi_calibration_evidence_portfolio.py.

Tested:

• Red step: python -m pytest backend\tests\test_sprint139_multi_aoi_calibration_evidence_portfolio.py -q failed while scripts/assemble_detection_calibration_evidence_portfolio.sh was absent.
• python -m pytest backend\tests\test_sprint139_multi_aoi_calibration_evidence_portfolio.py -q (1 passed)
• python -m pytest backend\tests\test_sprint139_multi_aoi_calibration_evidence_portfolio.py backend\tests\test_sprint138_calibration_evidence_bundle_smoke.py backend\tests\test_sprint137_browser_calibration_summary_evidence_script.py backend\tests\test_sprint125_detection_calibration_evidence_bundle.py -q (4 passed)
• bash -n scripts/assemble_detection_calibration_evidence_portfolio.sh
• bash scripts/assemble_detection_calibration_evidence_portfolio.sh --help
• bash -n scripts/export_detection_calibration_evidence.sh
• bash scripts/export_detection_calibration_evidence.sh --help
• python -m compileall backend/app
• bash scripts/run_readiness_check.sh (418 passed; frontend typecheck/build passed; Alembic head 202606120900; shell syntax gates passed)

Open:

• None for this pass.

Limitations:

• This is local/operator evidence packaging only. It does not run inference, call live production data by itself, mutate application data, add backend endpoints, change migrations, create QA metrics, promote thresholds, download models, add provider fetching or change frontend runtime behavior.
• The regression test uses mocked canonical QA evidence responses; real persisted QA evidence remains validated by running the portfolio assembler against live Detection Lab or calibration-sweep summaries.

Next recommended pass:

• Run the portfolio assembler against the existing Tower calibration summaries for at least two real AOIs, then use the portfolio JSON/Markdown as the first model-review handoff artifact before any further training or threshold promotion.

Sprint 138 Browser calibration evidence bundle smoke (2026-07-08)

Changed:

• Added scripts/smoke_detection_calibration_evidence_bundle.sh as a local operator smoke for the Detection Lab detection-calibration-summary.json to QA evidence bundle path.
• The smoke creates a temporary browser-style calibration summary, injects a temporary mock curl for canonical QA evidence endpoint responses, runs the real scripts/export_detection_calibration_evidence.sh exporter and validates the generated GeoJSON, summary JSON and HTML review artifacts.
• Added readiness syntax coverage for the smoke script.
• Updated scripts/README.md, CHANGELOG.md and docs/TODO.md.
• Added regression coverage in backend/tests/test_sprint138_calibration_evidence_bundle_smoke.py.

Tested:

• Red step: python -m pytest backend\tests\test_sprint138_calibration_evidence_bundle_smoke.py -q failed while scripts/smoke_detection_calibration_evidence_bundle.sh was absent.
• python -m pytest backend\tests\test_sprint138_calibration_evidence_bundle_smoke.py -q (1 passed)
• python -m pytest backend\tests\test_sprint138_calibration_evidence_bundle_smoke.py backend\tests\test_sprint137_browser_calibration_summary_evidence_script.py backend\tests\test_sprint125_detection_calibration_evidence_bundle.py -q (3 passed)
• bash -n scripts/smoke_detection_calibration_evidence_bundle.sh
• bash scripts/smoke_detection_calibration_evidence_bundle.sh --help
• python -m compileall backend/app
• bash scripts/run_readiness_check.sh (417 passed; frontend typecheck/build passed; Alembic head 202606120900; shell syntax gates passed)
• Pushed commit 47d3587 to main.
• Tower repo fast-forwarded to 47d3587; bash -n scripts/smoke_detection_calibration_evidence_bundle.sh passed on Tower.
• Tower operator smoke passed: bash scripts/smoke_detection_calibration_evidence_bundle.sh wrote mocked evidence artifacts under /mnt/user/appdata/geointel/artifacts/detection-calibration-smoke/20260708T115724Z/evidence with 4 evidence features across matched candidate, matched reference, false positive and false negative roles.
• Tower runtime remained healthy on 0.0.0.0:1202->80/tcp; no app rebuild was required because this pass changed operator scripts/docs/tests only.

Open:

• None for this pass.

Limitations:

• This is local/operator evidence tooling only. It does not call live production data, mutate application data, add backend endpoints, change migrations, rerun inference, create QA metrics, promote thresholds, download models, add provider fetching or change frontend runtime behavior.
• The smoke uses mocked canonical evidence responses by design; real persisted QA evidence is still validated by running export_detection_calibration_evidence.sh against a live browser or sweep summary.

Next recommended pass:

• Add a real multi-AOI calibration evidence capture convention: one folder per AOI/model/threshold matrix with browser summary, evidence bundle and operator notes, so model promotion decisions are based on comparable persisted artifacts rather than isolated runs.

Sprint 137 Browser calibration summary evidence bundle handoff (2026-07-08)

Changed:

• Extended scripts/export_detection_calibration_evidence.sh so it can consume Detection Lab detection-calibration-summary.json browser exports as well as the existing operator calibration_summary.json format.
• Added summary normalization for browser-exported rows, root project_id, persisted quality_check_id values and CALIBRATION_EVIDENCE_MODE=best fallback selection.
• Updated scripts/README.md, CHANGELOG.md and docs/TODO.md.
• Added regression coverage in backend/tests/test_sprint137_browser_calibration_summary_evidence_script.py.

Tested:

• Red step: python -m pytest backend\tests\test_sprint137_browser_calibration_summary_evidence_script.py -q failed while browser summary support was absent.
• python -m pytest backend\tests\test_sprint137_browser_calibration_summary_evidence_script.py backend\tests\test_sprint136_calibration_summary_export_ui.py backend\tests\test_sprint125_detection_calibration_evidence_bundle.py -q (3 passed)
• bash -n scripts/export_detection_calibration_evidence.sh
• python -m compileall backend/app
• bash scripts/run_readiness_check.sh (416 passed; frontend typecheck/build passed; Alembic head 202606120900; live smoke syntax passed)
• Tower deploy from commit 673f6c6 completed with GEOINTEL_INSTALL_AI=true; the all-in-one container is healthy and published on 0.0.0.0:1202->80/tcp.
• Deploy-time live migration smoke passed after the database became ready on attempt 4; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1, required runtime schema objects were present and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for frontend, API proxy and icon after one readiness retry.
• Live container check passed: docker ps --filter name=geointel reported geointel as healthy on 0.0.0.0:1202->80/tcp.
• Live YOLO preflight passed with local model configured, dependencies available, torch_version=2.12.1, ultralytics_version=8.4.90, will_download_models=false, will_run_inference=false and status=manifest_unavailable because no tile manifest was supplied.

Open:

• None for this pass.

Limitations:

• This is operator evidence tooling only. It does not add backend endpoints, change migrations, rerun inference, create new QA metrics, promote thresholds, mutate model configuration, download models, add provider fetching or change frontend runtime behavior.

Next recommended pass:

• Add a tiny local fixture smoke for the evidence bundle script that uses a saved browser-style summary plus mocked canonical evidence responses, so the bundle renderer itself is tested beyond static contract checks.

Sprint 136 Guided calibration summary export (2026-07-08)

Changed:

• Added a Download calibration summary action to the guided Detection Lab calibration progress table.
• The client-side JSON export includes calibration thresholds, persisted analysis_run_id, job_id, quality_check_id, metric values and QA evidence GeoJSON URLs.
• Added compact action-row styling and regression coverage in backend/tests/test_sprint136_calibration_summary_export_ui.py.
• Updated CHANGELOG.md and docs/TODO.md.

Tested:

• Red step: python -m pytest backend\tests\test_sprint136_calibration_summary_export_ui.py -q failed while the summary export helpers and button were absent.
• python -m pytest backend\tests\test_sprint136_calibration_summary_export_ui.py backend\tests\test_sprint135_calibration_evidence_handoff.py backend\tests\test_sprint134_guided_detection_calibration_runner.py -q (3 passed)
• python -m compileall backend/app
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh (415 passed; frontend typecheck/build passed; Alembic head 202606120900; live smoke syntax passed)
• Tower deploy from commit 8089df3 completed with GEOINTEL_INSTALL_AI=true; the all-in-one container is published on 0.0.0.0:1202->80/tcp.
• Deploy-time live migration smoke passed after the database became ready on attempt 2; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1, required runtime schema objects were present and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for frontend, API proxy and icon.
• Live bundle check passed: Download calibration summary is present in assets/index-5CClxMR_.js.
• Live YOLO preflight passed with local model configured, dependencies available, will_download_models=false, will_run_inference=false and status=manifest_unavailable because no tile manifest was supplied.

Open:

• None for this pass.

Limitations:

• This is a browser-side summary export only. It does not create server-side export records, rerun inference, create new QA metrics, promote thresholds, mutate model configuration, download models, add provider fetching or change API/database contracts.

Next recommended pass:

• Add a small import/consume path for downloaded calibration summaries in the existing operator evidence bundle script, so browser-exported summary JSON can be used directly from an operator workstation.

Sprint 135 Calibration evidence handoff (2026-07-08)

Changed:

• Added an Open evidence map action to successful guided detection calibration rows.
• Wired the Detection Lab action to the existing openQualityEvidenceOnMap flow, which loads persisted QA/QC evidence GeoJSON and opens the Map workspace overlay.
• Added compact table action styling and regression coverage in backend/tests/test_sprint135_calibration_evidence_handoff.py.
• Updated CHANGELOG.md and docs/TODO.md.

Tested:

• Red step: python -m pytest backend\tests\test_sprint135_calibration_evidence_handoff.py -q failed while the Detection Lab evidence handoff prop was absent.
• python -m pytest backend\tests\test_sprint135_calibration_evidence_handoff.py backend\tests\test_sprint134_guided_detection_calibration_runner.py backend\tests\test_sprint133_detection_threshold_calibration_ux.py backend\tests\test_sprint112_qa_evidence_overlay.py -q (7 passed)
• python -m compileall backend/app
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh (414 passed; frontend typecheck/build passed; Alembic head 202606120900; live smoke syntax passed)
• Tower deploy from commit 0c826ef completed with GEOINTEL_INSTALL_AI=true; the all-in-one container is published on 0.0.0.0:1202->80/tcp.
• Deploy-time live migration smoke passed after the database became ready on attempt 2; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1, required runtime schema objects were present and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for frontend, API proxy and icon.

Open:

• None for this pass.

Limitations:

• This pass adds review handoff only. It does not create new QA metrics, promote thresholds, mutate model configuration, download models, add provider fetching or change API/database contracts.

Next recommended pass:

• Add an operator-facing calibration evidence summary/export shortcut once the map handoff has been used on real AOI runs.

Sprint 134 External remote-sensing YOLO candidate benchmark (2026-07-07)

Changed:

• Added no production code and no repository-stored model weights.
• Evaluated the Hugging Face agademer/yolo-remote-sensing-photovoltaic model card as a candidate because it explicitly targets remote-sensing imagery and includes building as a class.
• Downloaded the YOLOv8l detection checkpoint manually as an operator/runtime asset:
  • source model: agademer/yolo-remote-sensing-photovoltaic
  • source file: yolo-remote-sensing-photovoltaic-v8l-solar-farms-and-cities-v20260331-detect-1000_epochs.pt
  • runtime file: /app/models/yolo-remote-sensing-photovoltaic-v8l-detect-1000.pt
  • catalog asset: yolo-remote-sensing-photovoltaic-v8l-detect-1000-pt
  • SHA256: 242ff4ab889569278f0eb9fcd22eb2c4bf2a52e48d05d89cc7cfa7941165d203
• Updated scripts/README.md, docs/TODO.md, docs/CODEX_EXECUTION_LOG.md and CHANGELOG.md with benchmark evidence and the decision not to promote this model.

Tested:

• Live API preflight passed for yolo-remote-sensing-photovoltaic-v8l-detect-1000-pt with status=ready, model_load_ok=true, manifest_valid=true, tile_paths_exist=true, will_download_models=false and will_run_inference=false.
• Live 45-run multi-sample QA matrix completed:
  • output: /mnt/user/appdata/geointel/artifacts/detection-quality-matrix/multi-sample/remote-sensing-v8l1000-live/multi_sample_quality_summary.json
  • command compared yolo-remote-sensing-photovoltaic-v8l-detect-1000-pt, geointel-building-yolov8n-expanded160e50-pt and geointel-building-yolov8n-hardneg160r8e40-pt over Geel, Mol, Turnhout, Retie and Kasterlee-bos with tile 640, overlap 64, thresholds 0.25/0.15/0.05.
  • dense-sample results for the external candidate:
    • Geel: best F1 0.0
    • Mol: best F1 0.010582010582010581
    • Turnhout: best F1 0.019070321811680575
    • Retie: best F1 0.0
  • Kasterlee-bos sparse result: best F1 0.25 with 1 detection, 1 match and 0 false positives at thresholds 0.25/0.15.
  • dense-sample winner remained geointel-building-yolov8n-expanded160e50-pt.
• Live 27-run hard-negative matrix completed:
  • output: /mnt/user/appdata/geointel/artifacts/detection-hard-negatives/remote-sensing-v8l1000-live/hard_negative_matrix_summary.json
  • Postel-bos: external candidate produced 0/1/3 detections at thresholds 0.25/0.15/0.05.
  • Lommel-heide: external candidate produced 0/0/0 detections.
  • Kasterlee-bos: external candidate produced 1/2/5 detections, cleaner than expanded160e50 and hardneg160r8e40 on that sparse AOI.

Open:

• None for this benchmark pass.

Limitations:

• The external model is very conservative on the current Kempen orthophoto/GRB benchmark and misses most dense buildings.
• It may be useful as evidence for a high-precision/sparse-review mode, but it is not a viable V1 default extraction model.
• The runtime .pt remains outside Git and must be managed as an operator asset.

Next recommended pass:

• Train a higher-capacity local detector from a stronger base model using the existing operator tile dataset, then gate it with both dense QA and hard-negative matrices before any model default changes.

Sprint 133 Hard-negative-balanced YOLO candidate (2026-07-07)

Changed:

• Hardened scripts/export_operator_yolo_tile_dataset.py with deterministic train-only background-negative repetition through --background-negative-repeat and OPERATOR_YOLO_BACKGROUND_NEGATIVE_REPEAT.
• Background-negative repetition applies only when is_negative=true, sample_role=background_candidate and split=train; validation tiles, positive tiles and normal reference samples are not duplicated.
• Added tile-level provenance fields sample_role, repeat_index and is_repeated_background_negative.
• Added regression coverage in backend/tests/test_sprint130_operator_yolo_tile_dataset.py.
• Updated scripts/README.md, docs/TODO.md, docs/CODEX_EXECUTION_LOG.md and CHANGELOG.md.

Tested:

• RED: python -m pytest backend\tests\test_sprint130_operator_yolo_tile_dataset.py -q failed before --background-negative-repeat and background_negative_repeat_count existed.
• python -m pytest backend\tests\test_sprint130_operator_yolo_tile_dataset.py -q passed.
• python -m py_compile scripts\export_operator_yolo_tile_dataset.py passed.
• python scripts\export_operator_yolo_tile_dataset.py --help passed.
• Live Tower hard-negative-balanced tile export passed:
  • dataset: /app/storage/operator-data/yolo-building-tile-hardneg160r8
  • source samples: 10
  • tile size: 160
  • stride: 80
  • negative keep ratio: 1.0
  • background negative repeat: 8
  • exported tiles: 864
  • positive tiles: 260
  • negative tiles: 604
  • labels: 11213
  • train tiles: 756
  • validation tiles: 108
• Live Tower 40-epoch CPU training passed:
  • output model: /app/models/geointel-building-yolov8n-hardneg160r8e40.pt
  • catalog asset: geointel-building-yolov8n-hardneg160r8e40-pt
  • SHA256: 7a77bd9f68e4c3927ffc8a8cd978a81067b02f42cffe77ada5334b5f8dbb6b50
  • final validation: precision 0.403, recall 0.378, mAP50 0.301, mAP50-95 0.0944
• Live API preflight passed for geointel-building-yolov8n-hardneg160r8e40-pt with status=ready, model_load_ok=true, manifest_valid=true, tile_paths_exist=true, will_download_models=false and will_run_inference=false.
• Live 60-run multi-sample QA matrix completed:
  • output: /mnt/user/appdata/geointel/artifacts/detection-quality-matrix/multi-sample/hardneg160r8e40-live/multi_sample_quality_summary.json
  • command compared geointel-building-yolov8n-hardneg160r8e40-pt, geointel-building-yolov8n-expanded160e50-pt, geointel-building-yolov8n-tile30-pt and yolov8s-building-segmentation-pt over Geel, Mol, Turnhout, Retie and Kasterlee-bos with tile 640, overlap 64, thresholds 0.25/0.15/0.05.
  • best overall score and recall remained Geel with geointel-building-yolov8n-expanded160e50-pt, precision 0.30333333333333334, recall 0.14748784440842788, F1 0.1984732824427481.
  • hardneg160r8e40 dense F1 lagged expanded160e50 on Geel (0.14394765539803708 vs 0.1984732824427481), Mol (0.11572700296735906 vs 0.1651651651651652), Turnhout (0.14911463187325258 vs 0.1938490214352283) and Retie (0.10538116591928251 vs 0.1569506726457399).
• Live 36-run hard-negative matrix completed:
  • output: /mnt/user/appdata/geointel/artifacts/detection-hard-negatives/hardneg160r8e40-live/hard_negative_matrix_summary.json
  • Postel-bos: hardneg160r8e40 produced 0/0/0 detections at thresholds 0.25/0.15/0.05; expanded160e50 produced 0/0/1.
  • Lommel-heide: hardneg160r8e40 produced 0/0/0 detections; expanded160e50 produced 0/0/10.
  • Kasterlee-bos: hardneg160r8e40 produced 5/9/25 detections; expanded160e50 produced 38/46/76.

Open:

• None for the hard-negative-balanced tile export contract itself.

Limitations:

• geointel-building-yolov8n-hardneg160r8e40-pt reduced false-positive pressure but regressed dense-AOI recall/F1. It should not become the V1 default.
• This remains operator tooling only. It does not add Training Studio, browser training controls, provider fetching, fake detections, model auto-provisioning or API contract changes.

Next recommended pass:

• Train or import a materially stronger aerial/Kempen building model candidate, then benchmark it against the same dense QA and hard-negative matrices before changing default model selection.

Sprint 132 Operator hard-negative detection matrix (2026-07-07)

Changed:

• Added scripts/run_operator_hard_negative_detection_matrix.sh.
• The script reads operator_samples_manifest.json, selects samples marked background_candidate or allow_empty_reference, uploads only the raster, generates a tile manifest, checks configured-YOLO preflight, runs POST /api/v1/detection/run and counts persisted detections.
• It intentionally does not upload reference vectors and does not call detection QA/QC endpoints, because background AOIs have no meaningful reference target.
• Added readiness shell-syntax coverage for the new script.
• Added regression coverage in backend/tests/test_sprint132_operator_hard_negative_matrix.py.
• Updated scripts/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• RED: python -m pytest backend\tests\test_sprint132_operator_hard_negative_matrix.py -q failed while scripts/run_operator_hard_negative_detection_matrix.sh did not exist.
• python -m pytest backend\tests\test_sprint132_operator_hard_negative_matrix.py -q passed.
• bash -n scripts/run_operator_hard_negative_detection_matrix.sh passed.
• Live Tower 27-run hard-negative matrix completed:
  • output: /mnt/user/appdata/geointel/artifacts/detection-hard-negatives/expanded160e50-live/hard_negative_matrix_summary.json
  • samples: Postel-bos, Lommel-heide and Kasterlee-bos
  • models: geointel-building-yolov8n-expanded160e50-pt, geointel-building-yolov8n-tile30-pt, yolov8s-building-segmentation-pt
  • tile size: 640
  • overlap: 64
  • thresholds: 0.25, 0.15, 0.05
• Live false-positive pressure results:
  • Postel-bos: expanded160e50 produced 0 detections at 0.25/0.15, 1 at 0.05; tile30 produced 0/0/1; yolov8s produced 0/3/6.
  • Lommel-heide: expanded160e50 produced 0 detections at 0.25/0.15, 10 at 0.05; tile30 produced 0/0/3; yolov8s produced 0/0/0.
  • Kasterlee-bos: expanded160e50 produced 38/46/76 detections at 0.25/0.15/0.05; tile30 produced 15/22/42; yolov8s produced 5/6/7.

Open:

• None for the hard-negative matrix tooling itself.

Limitations:

• Background matrix scores false-positive pressure from detection counts only. It does not calculate precision/recall/F1 because background candidates intentionally do not provide a full reference target.
• Kasterlee-bos still has 7 GRB features and is best interpreted as a sparse/hard-negative AOI, not a purely empty background tile.
• geointel-building-yolov8n-expanded160e50-pt should not be promoted to default model while Kasterlee-bos false-positive pressure remains high.

Next recommended pass:

• Train a hard-negative-balanced candidate: oversample sparse/background tiles, lower the dense-AOI max-detection bias, and rerun both dense QA matrix and hard-negative matrix before changing any default model selection.

Sprint 131 Operator sample expansion and negative-tile YOLO candidate (2026-07-07)

Changed:

• Extended scripts/prepare_operator_real_data_samples.py with sample_role and allow_empty_reference.
• Added reference AOIs for Herentals, Balen, Retie and Westerlo.
• Added background-candidate AOIs for Postel-bos, Lommel-heide and Kasterlee-bos. Background candidates can persist empty GRB FeatureCollections for negative-tile training, while normal reference samples still fail on empty GRB results.
• Added regression coverage in backend/tests/test_sprint131_operator_sample_expansion.py.
• Updated scripts/README.md, backend/README.md, docs/AI_PIPELINES.md, docs/TODO.md and CHANGELOG.md.

Tested:

• RED: python -m pytest backend\tests\test_sprint131_operator_sample_expansion.py -q failed before the new sample metadata and background candidates existed.
• python -m pytest backend\tests\test_sprint131_operator_sample_expansion.py -q passed.
• python -m py_compile scripts\prepare_operator_real_data_samples.py passed.
• python scripts\prepare_operator_real_data_samples.py --help passed.
• Live Tower operator sample preparation passed:
  • manifest: /app/storage/operator-data/operator_samples_manifest.json
  • samples: Geel 617, Mol 374, Turnhout 773, Herentals 665, Balen 309, Retie 592, Westerlo 334, Postel-bos 0, Lommel-heide 0, Kasterlee-bos 7 reference features.
• Live Tower expanded tile export passed:
  • dataset: /app/storage/operator-data/yolo-building-tile-expanded160
  • tile size: 160
  • stride: 80
  • exported tiles: 360
  • positive tiles: 260
  • negative tiles: 100
  • labels: 11213
  • train tiles: 252
  • validation tiles: 108
• Live Tower 50-epoch CPU training passed:
  • output model: /app/models/geointel-building-yolov8n-expanded160e50.pt
  • catalog asset: geointel-building-yolov8n-expanded160e50-pt
  • SHA256: bf6a5e8d25a62d784ee53764ea11d7ce89c4e7aeeac7588010e497b8d7dafb2b
  • final validation: precision 0.428, recall 0.389, mAP50 0.318, mAP50-95 0.106
• Live API preflight passed for geointel-building-yolov8n-expanded160e50-pt with status=ready, model_load_ok=true, manifest_valid=true, tile_paths_exist=true, will_download_models=false and will_run_inference=false.
• Live 45-run multi-sample QA matrix completed:
  • output: /mnt/user/appdata/geointel/artifacts/detection-quality-matrix/multi-sample/expanded160e50-live/multi_sample_quality_summary.json
  • command compared geointel-building-yolov8n-expanded160e50-pt, geointel-building-yolov8n-tile30-pt and yolov8s-building-segmentation-pt over Geel, Mol, Turnhout, Retie and Kasterlee-bos with tile 640, overlap 64, thresholds 0.25/0.15/0.05.
  • best overall score and recall: Geel, geointel-building-yolov8n-expanded160e50-pt, tile 640, threshold 0.05, 300 detections, 91 matches, 209 false positives, 526 false negatives, precision 0.30333333333333334, recall 0.14748784440842788, F1 0.1984732824427481.
  • dense-sample score winners: Geel, Mol, Turnhout and Retie all selected geointel-building-yolov8n-expanded160e50-pt.
  • hard-negative/sparse-sample winner: Kasterlee-bos selected yolov8s-building-segmentation-pt, threshold 0.25, F1 0.16666666666666666; the expanded local model produced too many false positives there.

Open:

• None for the sample-preparation and expanded-training runtime proof itself.

Limitations:

• This remains operator tooling only. It does not add Training Studio, browser training controls, provider fetching, fake detections, model auto-provisioning or API contract changes.
• geointel-building-yolov8n-expanded160e50-pt is the best tested candidate on dense operator AOIs, but it is still experimental and should not become the V1 default until hard-negative false positives improve.
• The next model pass should add more sparse/background AOIs, tune confidence/NMS/max-detection settings and compare a stronger architecture or longer run against the same persisted QA matrix.

Next recommended pass:

• Build a hard-negative model-quality pass: expand sparse/background AOIs, export a balanced tile dataset, train a stronger candidate, and rerun the multi-sample QA matrix with dense and background samples scored separately.

Sprint 130 Operator YOLO tile-level dataset tooling (2026-07-07)

Changed:

• Added scripts/export_operator_yolo_tile_dataset.py.
• The exporter reads operator_samples_manifest.json, opens each raster/reference pair, creates overlapping tile windows, clips GRB building bounding boxes into tile-local YOLO labels, writes dataset.yaml, and reports yolo_tile_dataset_summary.json.
• Added deterministic negative tile retention through negative_keep_ratio.
• Added readiness compile coverage for the tile exporter.
• Added regression coverage in backend/tests/test_sprint130_operator_yolo_tile_dataset.py.
• Updated scripts/README.md, backend/README.md, docs/AI_PIPELINES.md, docs/TODO.md and CHANGELOG.md.

Tested:

• RED: python -m pytest backend\tests\test_sprint130_operator_yolo_tile_dataset.py -q failed while scripts/export_operator_yolo_tile_dataset.py did not exist.
• python -m pytest backend\tests\test_sprint130_operator_yolo_tile_dataset.py -q passed.
• python scripts\export_operator_yolo_tile_dataset.py --help passed without requiring local GIS dependencies.
• python -m py_compile scripts\export_operator_yolo_tile_dataset.py passed.
• Live Tower tile export passed:
  • dataset: /app/storage/operator-data/yolo-building-tile-dataset
  • samples: Geel, Mol and Turnhout
  • tile size: 192
  • stride: 96
  • exported tiles: 75
  • positive tiles: 75
  • labels: 5321
  • validation split: Turnhout
• Live Tower 30-epoch CPU tile training passed:
  • output model: /app/models/geointel-building-yolov8n-tile30.pt
  • catalog asset: geointel-building-yolov8n-tile30-pt
  • SHA256: b9e228202500d7c85836d12a72e320f4f2f0cef24cbb1b5bf7fa78a6778390af
  • final validation: precision 0.208, recall 0.271, mAP50 0.122, mAP50-95 0.0308
• Live API preflight passed for geointel-building-yolov8n-tile30-pt with status=ready, model_load_ok=true, manifest_valid=true, tile_paths_exist=true, will_download_models=false and will_run_inference=false.
• Live 48-run multi-sample QA matrix completed:
  • output: /mnt/user/appdata/geointel/artifacts/detection-quality-matrix/multi-sample/20260707T190720Z/multi_sample_quality_summary.json
  • command compared geointel-building-yolov8n-tile30-pt with yolov8s-building-segmentation-pt over Geel, Mol and Turnhout, tile sizes 512/640, overlap 64, thresholds 0.50/0.25/0.15/0.05.
  • best overall score: Mol, geointel-building-yolov8n-tile30-pt, tile 640, threshold 0.15, 272 detections, 37 matches, 235 false positives, 337 false negatives, precision 0.13602941176470587, recall 0.09893048128342247, F1 0.11455108359133127.
  • best overall recall: Mol, geointel-building-yolov8n-tile30-pt, tile 512, threshold 0.05, 544 detections, 44 matches, 500 false positives, 330 false negatives, precision 0.08088235294117647, recall 0.11764705882352941, F1 0.09586056644880174.
  • best overall precision: Turnhout, yolov8s-building-segmentation-pt, tile 640, threshold 0.25, precision 0.4, recall 0.01034928848641656, F1 0.0201765447667087.
  • per-sample score winners: Geel geointel-building-yolov8n-tile30-pt F1 0.09671179883945842; Mol geointel-building-yolov8n-tile30-pt F1 0.11455108359133127; Turnhout yolov8s-building-segmentation-pt F1 0.09971777986829727.

Open:

• None for the tile exporter/training runtime proof itself.

Limitations:

• This remains operator tooling only. It does not add Training Studio, browser training controls, provider fetching, fake detections, model auto-provisioning or API contract changes.
• geointel-building-yolov8n-tile30-pt is an experimental local candidate, not a V1 default. It improves the operator-trained baseline materially but still has low recall and many false positives on the current 3-sample corpus.
• The current corpus is too small and all exported tiles were positive; the next model pass needs more AOIs and deliberate negative/background tiles.

Next recommended pass:

• Expand the operator sample corpus beyond Geel/Mol/Turnhout, include negative/background AOIs, regenerate the tile dataset, then train a longer/larger local model candidate and rerun the same persisted QA matrix.

Sprint 129 Operator YOLO training dataset tooling (2026-07-07)

Changed:

• Added scripts/export_operator_yolo_dataset.py to export prepared operator samples into a local YOLO detection dataset:
  • input manifest: operator_samples_manifest.json
  • output: dataset.yaml, images/train, labels/train, images/val, labels/val, yolo_dataset_summary.json
  • labels are derived from GRB building references with source_name=grb and reference_layer_name=buildings.
• Added scripts/train_operator_yolo_detector.sh as an explicit operator/runtime wrapper around a local Ultralytics training smoke:
  • requires OPERATOR_YOLO_DATASET_DIR
  • requires an existing YOLO_BASE_MODEL_PATH
  • writes a local TRAIN_MODEL_OUTPUT_PATH
  • uses PYTHON_BIN=python3 by default for the all-in-one container
  • writes training_summary.json.
• Added readiness coverage for exporter compile and train-wrapper shell syntax.
• Added regression coverage in backend/tests/test_sprint129_operator_yolo_training_dataset.py.
• Updated scripts/README.md, backend/README.md, docs/AI_PIPELINES.md, docs/TODO.md and CHANGELOG.md.

Tested:

• RED: python -m pytest backend\tests\test_sprint129_operator_yolo_training_dataset.py -q failed while the exporter and training wrapper contracts were incomplete.
• python -m pytest backend\tests\test_sprint129_operator_yolo_training_dataset.py -q passed.
• python scripts\export_operator_yolo_dataset.py --help passed without requiring local GIS dependencies.
• python -m py_compile scripts\export_operator_yolo_dataset.py passed.
• bash -n scripts/train_operator_yolo_detector.sh passed.
• Live Tower export passed: /app/storage/operator-data/yolo-building-dataset contains 3 images and 1427 labels from the current Geel/Mol/Turnhout samples.
• Live Tower training smoke passed with the existing /app/models/yolov8n.pt base model and wrote /app/models/geointel-building-yolov8n-smoke.pt.
• The first live smoke showed Ultralytics fetching an auxiliary plot font. The wrapper now sets plots=False and seeds Arial.ttf from an existing local system font before importing Ultralytics so the operator smoke path does not invoke plot/font network behavior.
• Full local readiness passed after the final wrapper hardening: bash scripts/run_readiness_check.sh ran 396 backend tests, frontend typecheck/build, Alembic head and smoke syntax checks.
• Pushed commits through 3936768 to Gitea and reset Tower /mnt/user/appdata/geointel to the same commit.
• Live Tower no-font smoke passed after deleting /app/storage/ultralytics/Ultralytics/Arial.ttf; the wrapper seeded the font locally and the 1-epoch run completed without the previous font fetch line.
• Live Tower final 8-epoch training smoke passed:
  • output model: /app/models/geointel-building-yolov8n-operator8.pt
  • catalog asset: geointel-building-yolov8n-operator8-pt
  • SHA256: 071d64c89a8fd28f915f7a5a553e2d164942721292693a1612c725284c6e2e1e
  • training summary: /app/storage/training/operator-yolo/geointel-building-yolov8n-operator8/training_summary.json.
• Live API preflight passed for geointel-building-yolov8n-operator8-pt with status=ready, model_load_ok=true, manifest_valid=true, tile_paths_exist=true, will_download_models=false and will_run_inference=false.
• Live 36-run multi-sample QA matrix completed:
  • command compared geointel-building-yolov8n-operator8-pt with yolov8s-building-segmentation-pt over Geel, Mol and Turnhout, tile sizes 512/640, thresholds 0.50/0.25/0.15.
  • output: /mnt/user/appdata/geointel/artifacts/detection-quality-matrix/multi-sample/20260707T033048Z/multi_sample_quality_summary.json.
  • geointel-building-yolov8n-operator8-pt produced zero detections for every sample/tile combination at thresholds 0.15 through 0.50.
  • best overall remained yolov8s-building-segmentation-pt on Mol, tile 640, threshold 0.15: 55 detections, 9 matches, 46 false positives, 365 false negatives, precision 0.16363636363636364, recall 0.02406417112299465, F1 0.04195804195804196.
• Live low-threshold operator8 matrix completed:
  • output: /mnt/user/appdata/geointel/artifacts/detection-quality-matrix/multi-sample/20260707T033230Z/multi_sample_quality_summary.json.
  • at threshold 0.05, operator8 still produced zero detections on all samples.
  • at threshold 0.01, operator8 produced many false positives and almost no matches; best case was Turnhout tile 512 with 280 detections, 2 matches, 278 false positives, precision 0.007142857142857143, recall 0.00258732212160414, F1 0.003798670465337132.

Open:

• None for the operator training/export tooling itself.

Limitations:

• This is operator tooling only. It does not add Training Studio, browser training controls, provider fetching, fake detections, model auto-provisioning or API contract changes.
• The 3-sample/8-epoch operator model is not production-useful and should not be activated as the default building detector.

Next recommended pass:

• Improve the training dataset before more model work: generate tile-level YOLO labels with enough positive/negative tiles, add more AOIs, then train a longer local model and rerun the same persisted QA matrix.

Sprint 128 Stronger building model runtime benchmark (2026-07-07)

Changed:

• Added keremberke/yolov8s-building-segmentation as an explicit runtime model asset on Tower:
  • path: /mnt/user/appdata/geointel/models/yolov8s-building-segmentation.pt
  • source: https://huggingface.co/keremberke/yolov8s-building-segmentation/resolve/main/best.pt
  • SHA256: a27af31654c6a4edbdc85581c33d93c13986b5919de7de410f8d85d801b3bb34
• Did not commit model weights to Git and did not add app-side model download behavior.
• Ran a stronger-candidate multi-sample matrix comparing:
  • yolov8n-building-segmentation-pt
  • yolov8s-building-segmentation-pt
  • samples: Geel, Mol, Turnhout
  • tile sizes: 512, 640
  • overlap: 64
  • thresholds: 0.50, 0.25, 0.15

Tested:

• Hugging Face API metadata check showed keremberke/yolov8s-building-segmentation is public, uses the Ultralytics library, exposes best.pt, and reports validation mAP@0.5 values for the keremberke/satellite-building-segmentation dataset.
• Live model asset catalog reported yolov8s-building-segmentation-pt, size_bytes=23814274, will_download_models=false, SHA256 a27af31654c6a4edbdc85581c33d93c13986b5919de7de410f8d85d801b3bb34.
• Live preflight passed with model_asset_id=yolov8s-building-segmentation-pt, check_model_load=true, model_file_exists=true, model_load_ok=true, will_download_models=false and will_run_inference=false.
• Tower 36-run matrix passed:
  • command: OPERATOR_SAMPLE_MANIFEST_PATH=storage/operator-data/operator_samples_manifest.json QUALITY_MODEL_ASSET_IDS="yolov8n-building-segmentation-pt yolov8s-building-segmentation-pt" QUALITY_TILE_SIZES="512 640" QUALITY_TILE_OVERLAPS="64" QUALITY_THRESHOLDS="0.50 0.25 0.15" bash scripts/run_multi_sample_detection_quality_matrix.sh http://192.168.10.150:1202
  • output: /mnt/user/appdata/geointel/artifacts/detection-quality-matrix/multi-sample/20260707T025932Z/multi_sample_quality_summary.json
  • best overall score: Mol, yolov8s-building-segmentation-pt, tile 640, threshold 0.15, 55 detections, 9 matches, 46 false positives, 365 false negatives, precision 0.16363636363636364, recall 0.02406417112299465, F1 0.04195804195804196.
  • best overall recall: Mol, yolov8s-building-segmentation-pt, tile 512, threshold 0.15, 77 detections, 9 matches, 68 false positives, 365 false negatives, precision 0.11688311688311688, recall 0.02406417112299465, F1 0.03991130820399113.
  • best overall precision: Turnhout, yolov8s-building-segmentation-pt, tile 640, threshold 0.25, 20 detections, 8 matches, 12 false positives, 765 false negatives, precision 0.4, recall 0.01034928848641656, F1 0.0201765447667087.
  • best Geel score: yolov8s-building-segmentation-pt, tile 640, threshold 0.15, F1 0.0187207488299532.
  • best Mol score: yolov8s-building-segmentation-pt, tile 640, threshold 0.15, F1 0.04195804195804196.
  • best Turnhout score remained yolov8n-building-segmentation-pt, tile 512, threshold 0.15, F1 0.03934426229508197.
• Best-score evidence export passed for every sample in the new matrix:
  • Geel evidence: /mnt/user/appdata/geointel/artifacts/detection-quality-matrix/multi-sample/20260707T025932Z/geel/calibration_evidence_review.html, 641 features, 611 false negatives, 18 false positives, 6 matched detections and 6 matched references.
  • Mol evidence: /mnt/user/appdata/geointel/artifacts/detection-quality-matrix/multi-sample/20260707T025932Z/mol/calibration_evidence_review.html, 429 features, 365 false negatives, 46 false positives, 9 matched detections and 9 matched references.
  • Turnhout evidence: /mnt/user/appdata/geointel/artifacts/detection-quality-matrix/multi-sample/20260707T025932Z/turnhout/calibration_evidence_review.html, 915 features, 755 false negatives, 124 false positives, 18 matched detections and 18 matched references.

Open:

• None for adding and benchmarking the yolov8s candidate.

Limitations:

• yolov8s improves precision and F1 on Geel/Mol but still misses most reference buildings. Best recall is about 2.4%, which is not sufficient for a usable V1 building extraction default.
• The model is a runtime artifact and remains outside Git.

Next recommended pass:

• Source or train a materially stronger aerial-building model. The GeoIntel pipeline is now good enough to benchmark candidates quickly, but the current public YOLO building-segmentation candidates are still too weak for the target Flemish orthophoto/GRB workflow.

Sprint 127 Multi-sample detection quality calibration tooling (2026-07-07)

Changed:

• Added scripts/prepare_operator_real_data_samples.py as an explicit operator/runtime helper for documented Geel, Mol and Turnhout real-data samples.
• The helper downloads small Digitaal Vlaanderen OMWRGBMRVL WMS Ortho GeoTIFFs and GRB OGC API Features GBG building GeoJSON references for the documented AOIs only.
• The helper writes operator_samples_manifest.json, sample metadata, source URLs and attribution under the runtime operator-data directory and reuses existing files by default.
• Added scripts/run_multi_sample_detection_quality_matrix.sh to run scripts/run_detection_quality_matrix.sh once per manifest sample.
• The multi-sample wrapper combines per-sample quality_matrix_summary.json files into multi_sample_quality_summary.json with best_overall_by_score, best_overall_by_recall, best_overall_by_precision and best_by_sample.
• Added readiness checks for Python compile and shell syntax.
• Added regression coverage in backend/tests/test_sprint127_operator_sample_quality_matrix.py.
• Updated scripts/README.md, backend/README.md, docs/AI_PIPELINES.md, docs/TODO.md and CHANGELOG.md.

Tested:

• RED: python -m pytest backend\tests\test_sprint127_operator_sample_quality_matrix.py -q failed because the sample-preparation and multi-sample scripts did not exist.
• RED: python -m pytest backend\tests\test_sprint127_operator_sample_quality_matrix.py::test_prepare_operator_real_data_samples_help_does_not_require_gis_dependencies -q failed because --help required missing GIS dependencies.
• python -m pytest backend\tests\test_sprint127_operator_sample_quality_matrix.py -q passed.
• python scripts\prepare_operator_real_data_samples.py --help passed without requiring local GIS dependencies.
• python -m py_compile scripts\prepare_operator_real_data_samples.py passed.
• bash -n scripts/run_multi_sample_detection_quality_matrix.sh passed.
• python -m pytest backend\tests\test_sprint127_operator_sample_quality_matrix.py backend\tests\test_sprint126_detection_quality_matrix.py backend\tests\test_sprint125_detection_calibration_evidence_bundle.py backend\tests\test_sprint124_detection_calibration_sweep.py -q passed.
• python scripts\smoke_docs.py passed.
• git diff --check passed.
• bash scripts/run_readiness_check.sh passed: 393 backend tests, frontend typecheck/build, Alembic head 202606120900, live smoke syntax checks and the new sample/multi-sample checks.
• Pushed commit 06dfc5f to Gitea and fast-forwarded Tower /mnt/user/appdata/geointel to the same commit.
• Tower sample preparation passed by running the new helper in the live geointel all-in-one container through stdin:
  • Geel reused existing runtime files: 617 GRB GBG reference features.
  • Mol was newly prepared: 374 GRB GBG reference features.
  • Turnhout was newly prepared: 773 GRB GBG reference features.
  • Manifest: /mnt/user/appdata/geointel/storage/operator-data/operator_samples_manifest.json.
• Tower multi-sample quality matrix passed:
  • command: OPERATOR_SAMPLE_MANIFEST_PATH=storage/operator-data/operator_samples_manifest.json QUALITY_MODEL_ASSET_IDS="yolov8n-building-segmentation-pt yolov8n-pt" QUALITY_TILE_SIZES="512 640" QUALITY_TILE_OVERLAPS="64" QUALITY_THRESHOLDS="0.50 0.15" bash scripts/run_multi_sample_detection_quality_matrix.sh http://192.168.10.150:1202
  • output: /mnt/user/appdata/geointel/artifacts/detection-quality-matrix/multi-sample/20260707T025303Z/multi_sample_quality_summary.json
  • run count: 24 real persisted workflows across 3 samples.
  • best overall score/recall: Turnhout, yolov8n-building-segmentation-pt, tile 512, overlap 64, threshold 0.15, 142 detections, 18 matches, 124 false positives, 755 false negatives, precision 0.1267605633802817, recall 0.02328589909443726, F1 0.03934426229508197.
  • best overall precision: Geel, yolov8n-building-segmentation-pt, tile 640, overlap 64, threshold 0.50, 4 detections, 1 match, 3 false positives, 616 false negatives, precision 0.25, recall 0.0016207455429497568, F1 0.0032206119162640897.
  • best Geel score: tile 512, threshold 0.15, 80 detections, 6 matches, 74 false positives, 611 false negatives, F1 0.017216642754662843.
  • best Mol score: tile 640, threshold 0.15, 77 detections, 7 matches, 70 false positives, 367 false negatives, F1 0.03104212860310421.
  • best Turnhout score: tile 512, threshold 0.15, 142 detections, 18 matches, 124 false positives, 755 false negatives, F1 0.03934426229508197.
  • generic yolov8n-pt produced zero building detections across every sample, tile size and threshold.
• Tower best-score evidence export passed for every sample:
  • Geel: /mnt/user/appdata/geointel/artifacts/detection-quality-matrix/multi-sample/20260707T025303Z/geel/calibration_evidence_review.html, 697 features, 611 false negatives, 74 false positives, 6 matched detections and 6 matched references.
  • Mol: /mnt/user/appdata/geointel/artifacts/detection-quality-matrix/multi-sample/20260707T025303Z/mol/calibration_evidence_review.html, 451 features, 367 false negatives, 70 false positives, 7 matched detections and 7 matched references.
  • Turnhout: /mnt/user/appdata/geointel/artifacts/detection-quality-matrix/multi-sample/20260707T025303Z/turnhout/calibration_evidence_review.html, 915 features, 755 false negatives, 124 false positives, 18 matched detections and 18 matched references.

Open:

• None for multi-sample tooling.

Limitations:

• This is operator tooling only. It does not add a live GRB provider, live orthophoto provider, application endpoint, migration, frontend feature, model download or fixture inference path.
• The prepared sample files are runtime artifacts under appdata/storage and remain excluded from Git.
• The three-sample benchmark confirms the current building-segmentation evaluation model is not extraction-quality for V1: best recall is only about 2.3% and false negatives dominate every sample.

Next recommended pass:

• Replace or add a stronger aerial/building model candidate and rerun the same multi-sample matrix; optionally add IoU-threshold sweeps after a model produces materially better candidate detections.

Sprint 126 Detection quality matrix tooling (2026-07-07)

Changed:

• Added scripts/run_detection_quality_matrix.sh as an operator-facing model/tile/threshold matrix for the configured-YOLO real-data path.
• The matrix reuses scripts/verify_real_data_detection_qa_workflow.sh for each row so every result is backed by persisted Project, Dataset, AnalysisRun, Detection, QualityCheck, Metric and Export records.
• The script accepts QUALITY_MODEL_ASSET_IDS, QUALITY_TILE_SIZES, QUALITY_TILE_OVERLAPS and QUALITY_THRESHOLDS, writes per-run logs and produces quality_matrix_summary.json.
• The summary reports model asset, tile size, overlap, confidence threshold, detection count, QA score, precision, recall, F1, mean IoU, matches, false positives and false negatives.
• Added best_by_score, best_by_recall and best_by_precision rankings for operator model-quality decisions.
• Added readiness syntax coverage and regression coverage in backend/tests/test_sprint126_detection_quality_matrix.py.
• Updated scripts/README.md, backend/README.md, docs/AI_PIPELINES.md, docs/TODO.md and CHANGELOG.md.

Tested:

• RED: python -m pytest backend\tests\test_sprint126_detection_quality_matrix.py -q failed because scripts/run_detection_quality_matrix.sh did not exist.
• python -m pytest backend\tests\test_sprint126_detection_quality_matrix.py -q passed.
• python -m pytest backend\tests\test_sprint126_detection_quality_matrix.py backend\tests\test_sprint124_detection_calibration_sweep.py backend\tests\test_sprint125_detection_calibration_evidence_bundle.py -q passed.
• bash -n scripts/run_detection_quality_matrix.sh passed.
• bash scripts/run_detection_quality_matrix.sh --help passed.
• python scripts\smoke_docs.py passed.
• git diff --check passed.
• bash scripts/run_readiness_check.sh passed: 390 backend tests, frontend typecheck/build, Alembic head 202606120900, live smoke syntax checks and the new matrix syntax check.
• Pushed commit e728f70 to Gitea and fast-forwarded Tower /mnt/user/appdata/geointel to the same commit.
• Tower live matrix passed against the Geel operator sample:
  • command: QUALITY_MODEL_ASSET_IDS="yolov8n-building-segmentation-pt yolov8n-pt" QUALITY_TILE_SIZES="512 640" QUALITY_TILE_OVERLAPS="64" QUALITY_THRESHOLDS="0.50 0.15" bash scripts/run_detection_quality_matrix.sh http://192.168.10.150:1202
  • output: /mnt/user/appdata/geointel/artifacts/detection-quality-matrix/20260707T023000Z/quality_matrix_summary.json
  • run count: 8
  • best by score and recall: yolov8n-building-segmentation-pt, tile 512, overlap 64, threshold 0.15, 80 detections, 6 matches, 74 false positives, 611 false negatives, precision 0.075, recall 0.009724473257698542, F1 0.017216642754662843.
  • best by precision: yolov8n-building-segmentation-pt, tile 640, overlap 64, threshold 0.50, 4 detections, 1 match, 3 false positives, 616 false negatives, precision 0.25, recall 0.0016207455429497568, F1 0.0032206119162640897.
  • generic yolov8n-pt produced zero building detections for all tested tile/threshold combinations.
• Tower best-run evidence export passed from the matrix summary:
  • /mnt/user/appdata/geointel/artifacts/detection-quality-matrix/20260707T023000Z/calibration_evidence.geojson
  • /mnt/user/appdata/geointel/artifacts/detection-quality-matrix/20260707T023000Z/calibration_evidence_summary.json
  • /mnt/user/appdata/geointel/artifacts/detection-quality-matrix/20260707T023000Z/calibration_evidence_review.html
  • evidence features: 697 total, 611 false negatives, 74 false positives, 6 matched detections and 6 matched references.

Open:

• None for matrix tooling.

Limitations:

• This is operator benchmarking tooling only. It does not change inference behavior, add model downloads, seed fixture detections, fetch providers, change API contracts or change migrations.
• A single Geel sample is not enough to declare a production V1 building-extraction baseline; additional orthophoto/reference samples are still needed before picking defaults.

Next recommended pass:

• Add at least two more local orthophoto/reference samples and run the same quality matrix before choosing V1 defaults; the current Geel evidence says runtime plumbing works, but the active model is still not extraction-quality.

Sprint 125 Detection calibration evidence bundle (2026-07-07)

Changed:

• Added scripts/export_detection_calibration_evidence.sh to turn a persisted detection calibration summary into visual QA evidence artifacts.
• The script reads calibration_summary.json, fetches the existing project quality-check evidence GeoJSON endpoint for each quality_check_id, enriches features with threshold/score/provenance and writes:
  • calibration_evidence.geojson
  • calibration_evidence_summary.json
  • calibration_evidence_review.html
• The HTML review artifact renders a simple SVG overview with distinct roles for match_candidate, match_reference, false_positive and false_negative.
• Registered the script in scripts/run_readiness_check.sh as a syntax check.
• Added regression coverage in backend/tests/test_sprint125_detection_calibration_evidence_bundle.py.
• Updated scripts/README.md, backend/README.md, docs/AI_PIPELINES.md, docs/TODO.md and CHANGELOG.md.

Validation:

• RED: python -m pytest backend/tests/test_sprint125_detection_calibration_evidence_bundle.py -q failed because scripts/export_detection_calibration_evidence.sh did not exist.
• python -m pytest backend/tests/test_sprint125_detection_calibration_evidence_bundle.py -q passed.
• bash -n scripts/export_detection_calibration_evidence.sh passed.
• bash scripts/export_detection_calibration_evidence.sh --help passed.
• Missing-input guard printed usage and did not fetch evidence.
• bash scripts/run_readiness_check.sh passed: 389 backend tests, Alembic head check, frontend typecheck/build and shell syntax checks.
• Tower pulled commit ea8dcb2 with git pull --ff-only origin main.
• Tower evidence export passed against /mnt/user/appdata/geointel/artifacts/detection-calibration/20260707T002103Z/calibration_summary.json.
• Evidence artifacts written on Tower:
  • /mnt/user/appdata/geointel/artifacts/detection-calibration/20260707T002103Z/calibration_evidence.geojson
  • /mnt/user/appdata/geointel/artifacts/detection-calibration/20260707T002103Z/calibration_evidence_summary.json
  • /mnt/user/appdata/geointel/artifacts/detection-calibration/20260707T002103Z/calibration_evidence_review.html
• Evidence counts from the Geel calibration export:
  • total evidence features: 2555
  • false negatives: 2460
  • false positives: 79
  • matched detections: 8
  • matched references: 8
• Copied calibration_evidence_review.html and calibration_evidence_summary.json to the local ignored artifacts/detection-calibration/20260707T002103Z/ directory for inspection.
• Internal browser validation passed through a temporary local static server: the review page loaded with title GeoIntel Detection Calibration Evidence, 1 SVG, 2555 SVG paths, 4 calibration table rows and 0 console errors.

Open:

• None for evidence export tooling.

Limitations:

• This is operator tooling only. It does not rerun inference, change API behavior, add UI behavior, fetch providers, seed demo data or download models.
• The SVG review is a lightweight geometry overview, not a replacement for full MapLibre evidence review in the workbench.
• The evidence distribution confirms the current active model misses most reference buildings on this sample. This points to model suitability and/or tiling strategy as the next bottleneck, not runtime plumbing.

Next recommended pass:

• Add a model-quality decision pass: compare the current evaluation model against another building/aerial model or adjusted tile/overlap settings on at least two additional local orthophoto/reference samples.

Sprint 124 Detection calibration sweep tooling (2026-07-07)

Changed:

• Added scripts/run_detection_calibration_sweep.sh as an operator-facing confidence-threshold sweep for the configured-YOLO real-data path.
• The sweep reuses scripts/verify_real_data_detection_qa_workflow.sh once per threshold, so each row is backed by persisted Project, Dataset, AnalysisRun, Detection, QualityCheck, Metric and export records.
• The sweep fetches project quality-checks after each run and writes per-threshold summaries plus calibration_summary.json with detection count, QA score, precision, recall, F1, mean IoU, matches, false positives and false negatives.
• Registered the sweep in scripts/run_readiness_check.sh as a syntax check.
• Added regression coverage in backend/tests/test_sprint124_detection_calibration_sweep.py.
• Updated scripts/README.md, backend/README.md, docs/AI_PIPELINES.md, docs/TODO.md and CHANGELOG.md.

Validation:

• RED: python -m pytest backend/tests/test_sprint124_detection_calibration_sweep.py -q failed because scripts/run_detection_calibration_sweep.sh did not exist.
• python -m pytest backend/tests/test_sprint124_detection_calibration_sweep.py -q passed.
• bash -n scripts/run_detection_calibration_sweep.sh passed.
• bash scripts/run_detection_calibration_sweep.sh --help passed.
• Missing-input guard printed usage and did not start a live workflow.
• bash scripts/run_readiness_check.sh passed: 388 backend tests, Alembic head check, frontend typecheck/build and shell syntax checks.
• Tower pulled commit 590e597 with git pull --ff-only origin main and ran the default Geel calibration sweep.
• The first Tower sweep exposed that the summary script looked for f1_score while persisted metrics use f1; the script was patched with a metrics.get("f1") fallback and regression coverage.
• RED: python -m pytest backend/tests/test_sprint124_detection_calibration_sweep.py -q failed while the script lacked the f1 fallback.
• python -m pytest backend/tests/test_sprint124_detection_calibration_sweep.py -q passed after the fallback.
• Tower pulled commit e8cd463 and reran the Geel calibration sweep successfully:
  • output: artifacts/detection-calibration/20260707T002103Z/calibration_summary.json
  • threshold 0.50: 4 detections, score/F1 0.0032206119162640897, precision 0.25, recall 0.0016207455429497568, 1 match, 3 false positives, 616 false negatives
  • threshold 0.35: 9 detections, score/F1 0.003194888178913738, precision 0.1111111111111111, recall 0.0016207455429497568, 1 match, 8 false positives, 616 false negatives
  • threshold 0.25: 20 detections, score/F1 0.0031397174254317113, precision 0.05, recall 0.0016207455429497568, 1 match, 19 false positives, 616 false negatives
  • threshold 0.15: 54 detections, score/F1 0.014903129657228018, precision 0.09259259259259259, recall 0.008103727714748784, 5 matches, 49 false positives, 612 false negatives
  • best by score for this sample: threshold 0.15

Open:

• None for calibration tooling.

Limitations:

• The sweep is intentionally mutating and creates one real workflow run per threshold.
• It is calibration tooling only; it does not change inference, add model downloads, fetch providers, seed demo detections or change API/UI behavior.
• The current active building model still performs poorly on the Geel validation sample. Threshold 0.15 is best among the tested values, but recall remains under 1%; this is model/data-quality evidence, not a production-ready extraction baseline.

Next recommended pass:

• Inspect false-positive/false-negative evidence for the Geel runs, then add at least two more local orthophoto/reference samples before choosing V1 default confidence/IoU guidance.

Sprint 123 YOLO class and tile CRS normalization (2026-07-07)

Changed:

• Investigated the Geel real-data smoke that persisted zero detections despite the configured building model being available.
• Confirmed on Tower that /app/models/yolov8n-building-segmentation.pt reports model class Building and returns 4 raw detections at confidence 0.5 on the same real Geel tile manifest.
• Fixed configured-YOLO detection persistence so model class names are compared case-insensitively against class_filter, persisted as canonical lowercase domain classes, and preserve the original model class name in properties_json.model_class_name.
• Found a second live GIS correctness issue: generated tile manifests carried Lambert bounds/transforms but no CRS, so detection GeoJSON could expose EPSG:31370 coordinates as if they were EPSG:4326.
• Fixed raster tile manifest generation to include crs, source_crs and dataset_crs on the manifest and crs on each tile entry when the source raster CRS is known.
• Added regression coverage in backend/tests/test_sprint8b_yolo_foundation.py and backend/tests/test_raster_operations_service.py.

Validation:

• RED: python -m pytest backend/tests/test_sprint8b_yolo_foundation.py::test_yolo_class_filter_is_case_insensitive_and_persists_canonical_class -q failed with detection_count=0 because Building did not match building.
• python -m pytest backend/tests/test_sprint8b_yolo_foundation.py::test_yolo_class_filter_is_case_insensitive_and_persists_canonical_class -q passed.
• python -m pytest backend/tests/test_sprint8b_yolo_foundation.py backend/tests/test_model_asset_catalog.py backend/tests/test_sprint121_real_data_detection_qa_smoke.py backend/tests/test_sprint122_raster_upload_metadata_mapping.py -q passed: 20 tests.
• RED: python -m pytest backend/tests/test_raster_operations_service.py::test_raster_tile_returns_manifest_payload -q failed because the tile manifest had no crs.
• python -m pytest backend/tests/test_raster_operations_service.py::test_raster_tile_returns_manifest_payload -q passed.
• python -m compileall backend/app passed.
• bash scripts/run_readiness_check.sh passed: 387 backend tests, Alembic head check, frontend typecheck/build and shell syntax checks.
• Tower deploy from commit 71c2cd9 passed with GEOINTEL_INSTALL_AI=true.
• Deploy-time live migration smoke passed; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1 and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for http://192.168.10.150:1202.
• Real-data smoke after the class-normalization deploy passed and persisted 4 detections:
  • project: cb80638d-dbef-48ac-b19c-cec7c3efc96e
  • raster dataset: ae0ff76d-70c0-404f-b777-54d14517179a
  • reference dataset: 8ac01b4f-bd6a-4d6a-b625-a0950ae0f3eb
  • analysis run: 7ba34274-411d-45e3-8f54-c37baec598b1
  • quality check: 66907e6a-9ed7-4b0c-976f-5ad1ba9b8b7a
  • detection export: 516d37a3-2305-48a2-a3dd-b56f70eb055e
  • persisted detections used canonical class_name=building and preserved model_class_name=Building.
• Tower deploy from commit bc87681 passed with GEOINTEL_INSTALL_AI=true.
• Deploy-time live migration smoke passed; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1 and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for http://192.168.10.150:1202.
• Real-data smoke after the tile-CRS deploy passed and persisted 4 detections:
  • project: 6ced26f3-4486-451e-a05e-26a8f859e361
  • raster dataset: d54c3420-03d4-45a2-9501-bf1cce165a89
  • reference dataset: 319dad51-15e9-4ae6-815b-8e39023ed962
  • manifest: /app/storage/tiles/6ced26f3-4486-451e-a05e-26a8f859e361/d54c3420-03d4-45a2-9501-bf1cce165a89/721c9557-5910-4994-b0ac-ba3418d12246/manifest.json
  • analysis run: e99bcd07-ba48-4ebe-9ba9-24a94a8c1dcb
  • quality check: 32c8252b-fe60-4062-9639-125c62ca677f
  • detection export: 9d3a7f60-07a2-415c-9f75-a3c1988d0904
• Verified the new tile manifest now carries crs=EPSG:31370, source_crs=EPSG:31370, dataset_crs=EPSG:31370 and per-tile crs=EPSG:31370.
• Verified Detection GeoJSON now returns WGS84 coordinates around Geel instead of raw Belgian Lambert coordinates.
• Live detection QA persisted honest metrics for the Geel sample: 1 match, 3 false positives, 616 false negatives and score 0.0032206119162640897.

Open:

• None for availability, persistence and CRS propagation.

Limitations:

• This fixes class routing, persistence and future tile manifest CRS propagation. Existing tile manifests generated before this fix remain missing CRS and should be regenerated before AI runs.
• The active building model is operational but not calibrated for production-quality local Belgian/Kempen orthophoto extraction. The Geel sample proves end-to-end persistence and QA, while the 1/617 reference match result shows that confidence thresholds, tiling strategy, class mapping and IoU defaults still need model-quality calibration.

Next recommended pass:

• Run a detection calibration pass on several local orthophoto/reference samples: sweep confidence thresholds, inspect false positives/false negatives, tune tile size/overlap where needed and record a practical V1 baseline.

Sprint 122 Real operator data availability and raster metadata fix (2026-07-07)

Changed:

• Created real operator validation files on Tower under /mnt/user/appdata/geointel/storage/operator-data.
• Generated /mnt/user/appdata/geointel/storage/operator-data/geel_orthophoto_wms_512.tif from the official Digitaal Vlaanderen OMWRGBMRVL WMS Ortho layer for a 500 m x 500 m AOI around Geel.
• Generated /mnt/user/appdata/geointel/storage/operator-data/geel_grb_gbg_buildings.geojson from the official Digitaal Vlaanderen GRB OGC API Features GBG building collection for the same AOI; it contained 617 features.
• Fixed DatasetService raster upload metadata mapping so extracted bounds, resolution and dtype populate persisted bounds_json, resolution_json and bands_json.
• Added backend/tests/test_sprint122_raster_upload_metadata_mapping.py.

Validation:

• RED: python -m pytest backend/tests/test_sprint122_raster_upload_metadata_mapping.py -q failed because uploaded raster bounds_json was None.
• python -m pytest backend/tests/test_sprint122_raster_upload_metadata_mapping.py -q passed.
• python -m compileall backend/app passed.
• bash scripts/run_readiness_check.sh passed: 386 backend tests, Alembic head check, frontend typecheck/build and shell syntax checks.
• Tower deploy from commit 2468945 passed with GEOINTEL_INSTALL_AI=true.
• Deploy-time live migration smoke passed; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1 and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for http://192.168.10.150:1202.
• Real-data smoke passed:
  • command: REAL_RASTER_PATH=/mnt/user/appdata/geointel/storage/operator-data/geel_orthophoto_wms_512.tif REAL_REFERENCE_VECTOR_PATH=/mnt/user/appdata/geointel/storage/operator-data/geel_grb_gbg_buildings.geojson bash scripts/verify_real_data_detection_qa_workflow.sh http://192.168.10.150:1202
  • project: e1ec201b-bd83-4c95-be5a-f7225902d3c5
  • raster dataset: fca14d23-bc0c-4b1f-8bfa-ba968f2e05d0
  • reference dataset: 5bb4a9b4-2a4e-4680-a729-777a75ecd51f
  • model asset: yolov8n-building-segmentation-pt
  • manifest: /app/storage/tiles/e1ec201b-bd83-4c95-be5a-f7225902d3c5/fca14d23-bc0c-4b1f-8bfa-ba968f2e05d0/0f29f110-951f-48eb-a2fb-636b587e31ee/manifest.json
  • analysis run: c6798559-590c-42e3-a192-45a2409fa832
  • detections: 0
  • quality check: cd0d13f9-f6ce-4acf-8859-7cb7aa666125
  • detection export: 0081c230-1766-4ff3-8df0-e47236f529d1

Limitations:

• The workflow is operational against real operator data. A follow-up class-normalization pass found that the active evaluation model returned Building while the workflow filtered on building; see Sprint 123.
• The prepared files are runtime artifacts on Tower, not repository fixtures.

Next recommended pass:

• Redeploy the class-normalization fix, rerun the real-data smoke and calibrate confidence/IoU thresholds against persisted detection and QA metrics.

Sprint 121 Real data detection and QA workflow smoke (2026-07-07)

Changed:

• Added scripts/verify_real_data_detection_qa_workflow.sh for live-runtime validation with operator-provided real GIS inputs.
• The smoke creates a project, uploads a real GeoTIFF-style raster as a source dataset, uploads a real reference-building GeoJSON/JSON as dataset_role=reference, validates CRS/bounds/features, tiles the raster, selects a mounted local model asset, runs read-only YOLO preflight, runs configured YOLO detection, runs detection QA against persisted vector_features, and exports the detection run GeoJSON.
• Registered the smoke in scripts/run_readiness_check.sh as a syntax check only, so normal readiness does not require real orthophotos, reference vectors, optional AI dependencies or model files.
• Documented Tower usage and limitations in scripts/README.md, backend/README.md, docs/AI_PIPELINES.md, docs/TODO.md and CHANGELOG.md.

Validation:

• RED: python -m pytest backend/tests/test_sprint121_real_data_detection_qa_smoke.py -q failed while scripts/verify_real_data_detection_qa_workflow.sh did not exist.
• python -m pytest backend/tests/test_sprint121_real_data_detection_qa_smoke.py -q passed: 1 test.
• bash -n scripts/verify_real_data_detection_qa_workflow.sh passed.
• bash scripts/verify_real_data_detection_qa_workflow.sh --help passed and printed required REAL_RASTER_PATH and REAL_REFERENCE_VECTOR_PATH usage.
• python -m compileall backend/app passed.
• python scripts/smoke_docs.py passed.
• bash scripts/run_readiness_check.sh passed: 385 backend tests, Alembic head check, frontend typecheck, frontend production build and shell syntax checks.
• cd backend && python -m alembic upgrade head --sql passed.
• Missing-input guard passed: bash scripts/verify_real_data_detection_qa_workflow.sh http://localhost:1202 returned exit code 2 and printed usage.
• Local docker compose config could not run in this Windows Codex environment because the docker command is not installed.

Limitations:

• The full real-data smoke was not executed in this Codex workspace because no operator-provided real GeoTIFF and reference GeoJSON were found locally.
• The script enforces real inputs and never seeds demo data, enables fixture detections, fetches live GRB/OSM/Sentinel data or downloads model weights.

Next recommended pass:

• Place a target orthophoto/GeoTIFF and matching reference-building GeoJSON under the Tower appdata path and run REAL_RASTER_PATH=... REAL_REFERENCE_VECTOR_PATH=... bash scripts/verify_real_data_detection_qa_workflow.sh http://192.168.10.150:1202.

Sprint 120 Model asset detection workflow smoke (2026-07-06)

Changed:

• Added scripts/verify_model_asset_detection_workflow.sh to validate the configured-YOLO runtime path against a live Docker/Tower deployment.
• The smoke seeds the explicit offline demo workflow, creates a raster tile manifest, selects the active local model asset from GET /api/v1/detection/model-assets, verifies read-only YOLO preflight, submits the existing detection run endpoint and checks persisted AnalysisRun, Detection list and Detection GeoJSON outputs.
• Registered the script in scripts/run_readiness_check.sh as a syntax check only, so ordinary readiness runs remain valid on machines without optional AI dependencies or mounted model files.
• Documented the smoke in scripts/README.md, backend/README.md, docs/AI_PIPELINES.md, docs/TODO.md and CHANGELOG.md.

Validation:

• RED: python -m pytest backend/tests/test_sprint120_model_asset_detection_workflow_smoke.py -q failed because scripts/verify_model_asset_detection_workflow.sh did not exist yet.
• python -m pytest backend/tests/test_sprint120_model_asset_detection_workflow_smoke.py -q passed: 1 test.
• bash -n scripts/verify_model_asset_detection_workflow.sh passed.
• Live Tower smoke passed: bash scripts/verify_model_asset_detection_workflow.sh http://192.168.10.150:1202.
• Live smoke selected model_asset_id=yolov8n-building-segmentation-pt, generated manifest /app/storage/tiles/c0b00f1f-80bf-4992-be94-f5e5e6f6bf63/f9160f51-ee78-43b3-9353-d5390576fa1d/e9acd488-c376-45ed-b259-0dd79886f21e/manifest.json, persisted analysis run 7f9e7ecb-c43d-4ed3-9f98-424bc0317805 and returned detection_count=0.
• python -m compileall backend/app passed.
• cd backend && python -m pytest -q passed: 384 tests with the existing Pydantic model_* namespace warnings.
• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• cd backend && python -m alembic heads passed: 202606120900 (head).
• cd backend && python -m alembic upgrade head --sql passed.
• bash scripts/run_readiness_check.sh passed: 384 backend tests, frontend typecheck/build, API contract audit, Alembic head and shell syntax checks.
• Live browser/API smoke passed: bash scripts/verify_browser_runtime.sh http://192.168.10.150:1202.
• Live GIS capability smoke passed: bash scripts/verify_gis_runtime.sh http://192.168.10.150:1202.
• Live raster workflow smoke passed: bash scripts/verify_demo_raster_workflow.sh http://192.168.10.150:1202.
• Live workbench default-state smoke passed: bash scripts/verify_workbench_default_state.sh http://192.168.10.150:1202.
• Live workbench backing-state smoke passed: bash scripts/verify_workbench_interactions.sh http://192.168.10.150:1202.
• Live demo/export workflow smoke passed: bash scripts/verify_demo_export_workflow.sh http://192.168.10.150:1202.
• bash scripts/verify_ai_handoff_interactions.sh http://192.168.10.150:1202 could not run in this local Codex shell because Node cannot import Playwright; the script remains syntax-checked in readiness and the internal browser was used for live visual verification instead.
• Internal browser validation passed on http://192.168.10.150:1202: AI Labs rendered Detection Lab and Segmentation Lab, selecting yolo-configured showed the Local model assets selector with yolov8n-building-segmentation (active) and yolov8n, no-download copy was visible and no console errors were emitted.
• git push origin main pushed commit b2fe7fa.
• Tower deploy from commit b2fe7fa completed with GEOINTEL_INSTALL_AI=true; the all-in-one container is published on 0.0.0.0:1202->80/tcp.
• Deploy-time live migration smoke passed after the database became ready on attempt 3; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1 and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for http://192.168.10.150:1202, API proxy and icon.
• Post-deploy live model asset detection workflow smoke passed: bash scripts/verify_model_asset_detection_workflow.sh http://192.168.10.150:1202 selected yolov8n-building-segmentation-pt, persisted analysis run f758c992-4fb8-4dca-ab29-5c216cb14078 and returned detection_count=0.

Limitations:

• The smoke proves the configured-YOLO runtime path, provenance and persistence. It does not prove production model quality because it runs against the synthetic demo raster.
• Real operational validation still requires uploading a georeferenced Kempen orthophoto/GeoTIFF, running the configured building model on that raster and comparing persisted detections against reference building vectors through QA/QC.

Next recommended pass:

• Create the real-data validation path for orthophoto upload, tile generation, configured building-model run and reference-vector QA/QC.

Sprint 118 Local model and reference catalog clarity (2026-07-06)

Changed:

• Added a read-only backend model asset catalog through GET /api/v1/detection/model-assets.
• Added YOLO_MODELS_DIR to backend settings, Compose, Unraid env examples and all-in-one runtime startup so /app/models is the explicit model catalog directory.
• Extended configured YOLO preflight and detection runs with optional model_asset_id, resolved server-side against the model asset catalog.
• Detection jobs and analysis runs now persist selected model asset ID, path and SHA-256 in parameters for reproducibility.
• Detection Lab now loads local model assets, selects the active model by default and lets operators choose a cataloged local model file for yolo-configured.
• Provider Capabilities now distinguishes GRB/OSM/manual/fixture reference-data sources from AI model choices.
• Updated docs/API_CONTRACTS.md, docs/AI_PIPELINES.md, backend/README.md, frontend/README.md, deploy/unraid/README.md, scripts/README.md, docs/TODO.md and CHANGELOG.md.
• Added design/plan documents under docs/superpowers/.

Validation:

• RED: python -m pytest backend/tests/test_model_asset_catalog.py -q failed before implementation because app.services.model_asset_catalog_service did not exist.
• python -m pytest backend/tests/test_model_asset_catalog.py -q passed: 5 tests.
• RED: python -m pytest backend/tests/test_sprint118_yolo_preflight_ui.py -q failed before frontend wiring because the model asset types/API/hook/UI were absent.
• python -m pytest backend/tests/test_sprint118_yolo_preflight_ui.py -q passed: 2 tests.
• RED: runtime config tests failed before YOLO_MODELS_DIR was added to env examples, Unraid runtime and scripts/configure_yolo_model.py.
• python -m pytest backend/tests/test_docker_runtime_config.py::test_env_example_uses_runtime_env_names_read_by_backend_and_frontend backend/tests/test_docker_runtime_config.py::test_unraid_deploy_passes_ai_build_arg_and_yolo_runtime_env backend/tests/test_sprint119_yolo_model_configuration.py -q passed: 6 tests.
• python -m compileall backend/app passed.
• cd backend && python -m pytest -q passed: 383 tests.
• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• python scripts/audit_api_contracts.py passed: 81 implemented routes match docs; 2 explicit non-envelope endpoints tracked.
• bash scripts/run_readiness_check.sh passed: 383 backend tests plus frontend typecheck/build, API contract audit, Alembic head and shell syntax checks.
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh; bash -n deploy/unraid/run-dockerman-container.sh; bash -n deploy/unraid/all-in-one-start.sh; bash -n scripts/deploy_tower.sh passed.
• Local docker compose config could not run because Docker is not installed in this Windows Codex environment; Tower deploy validation remains required.
• git push origin main passed and pushed commits through 6e2a8cb.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1 with GEOINTEL_INSTALL_AI=true passed and redeployed Tower from commit 6e2a8cb.
• Deploy-time live migration smoke passed on Tower; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1 and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for http://192.168.10.150:1202, API proxy and icon.
• Live GET http://192.168.10.150:1202/api/v1/detection/model-assets returned two cataloged local assets: yolov8n-building-segmentation-pt active with SHA-256 152d6a9c5c76c9f2fd2fd5cc167efaed7c8c02e31002b15415899710f1d71f98, and yolov8n-pt.
• Live GET /api/v1/detection/yolo/preflight?model_asset_id=yolov8n-pt resolved /app/models/yolov8n.pt, reported dependencies available, no downloads and no inference.
• Live GET /api/v1/detection/yolo/preflight?model_asset_id=yolov8n-building-segmentation-pt&check_model_load=true passed model load smoke with model_load_ok=true, torch_version=2.12.1, ultralytics_version=8.4.89, no downloads and no inference.
• Internal browser validation passed on http://192.168.10.150:1202: Detection Lab showed Local model assets, listed yolov8n-building-segmentation (active) and yolov8n, auto-selected the active asset, showed no-download copy, and emitted no console errors.
• Internal browser validation passed for System/Provider Capabilities: Official reference sources, GRB, OSM and not AI model choices copy were visible with no console errors.

Limitations:

• The catalog is intentionally filesystem-backed and read-only. It does not download, validate semantic class metadata, train models or manage model lifecycle records in the database.
• GRB/OSM remain provider capabilities only; no live external fetching was added.

Next recommended pass:

• Redeploy Tower, verify /api/v1/detection/model-assets, confirm Detection Lab shows the local model picker, then continue with real raster/model workflow validation on non-synthetic imagery.

Sprint 117 Reusable GIS run and AI runtime opt-in (2026-07-05)

Changed:

• Added a Map workspace full-run mode selector with Create new dataset/export and Reuse latest saved dataset for QA.
• Reuse mode runs QA/QC against the latest saved derived map-selection dataset without creating another derived dataset/export pair.
• Added opt-in Docker and Unraid AI build support through GEOINTEL_INSTALL_AI=true; default builds still install only the GIS runtime.
• Passed YOLO runtime environment variables and a /app/models volume into the all-in-one Unraid container so local PyTorch/Ultralytics models can be mounted explicitly.
• Hardened the AI image path after Tower validation showed torch imported but ultralytics failed on a missing OpenCV native library. The Dockerfiles now include the required OpenCV runtime shared libraries and YOLO dependency detection performs real imports instead of find_spec checks.
• Added a writable YOLO_CONFIG_DIR default under application storage after Tower validation showed Ultralytics otherwise falls back to /tmp because root config is not writable in the container.
• Added YOLO preflight runtime diagnostics so operators can see dependency assumption state, model directory, YOLO_CONFIG_DIR, installed torch/ultralytics package versions and CUDA availability without loading a model, running inference or downloading weights.
• Added a read-only GET /api/v1/detection/yolo/preflight endpoint and Detection Lab YOLO runtime preflight panel so browser operators can inspect live AI runtime readiness without loading a model, running inference or downloading weights.
• Updated .env.example, backend/README.md, frontend/README.md, scripts/README.md, docs/AI_PIPELINES.md, docs/TODO.md and CHANGELOG.md.
• Added regression coverage in backend/tests/test_sprint116_operational_gis_map_workflow.py, backend/tests/test_sprint8b_yolo_foundation.py and backend/tests/test_docker_runtime_config.py.

Validation:

• RED: python -m pytest backend\tests\test_sprint116_operational_gis_map_workflow.py backend\tests\test_docker_runtime_config.py -q failed before implementation because fullWorkflowMode, AI build args and YOLO runtime env wiring were absent.
• python -m pytest backend\tests\test_sprint116_operational_gis_map_workflow.py backend\tests\test_docker_runtime_config.py -q passed: 22 tests.
• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• python -m compileall backend/app passed.
• python -m py_compile scripts\yolo_preflight.py backend\scripts\yolo_preflight.py passed.
• python -m pytest backend\tests\test_sprint31_unraid_template.py backend\tests\test_docker_runtime_config.py -q passed: 27 tests.
• RED: python -m pytest backend\tests\test_sprint8b_yolo_foundation.py backend\tests\test_docker_runtime_config.py -q failed before the runtime hardening because YOLO dependency detection still used find_spec and the Dockerfiles lacked OpenCV native runtime libraries.
• python -m pytest backend\tests\test_sprint8b_yolo_foundation.py backend\tests\test_docker_runtime_config.py -q passed: 30 tests.
• RED: python -m pytest backend\tests\test_docker_runtime_config.py -q failed before YOLO_CONFIG_DIR wiring because the Compose, Unraid and startup paths did not define a writable Ultralytics config directory.
• python -m pytest backend\tests\test_docker_runtime_config.py -q passed: 21 tests.
• python -m compileall backend/app passed after the AI runtime hardening.
• cd backend && python -m pytest -q passed: 367 tests.
• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• bash scripts/run_readiness_check.sh passed: 367 backend tests plus frontend typecheck/build.
• cd backend && python -m alembic heads passed: 202606120900 (head).
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh; bash -n scripts/deploy_tower.sh; bash -n deploy/unraid/run-dockerman-container.sh passed.
• Local Codex host could not run docker compose config because Docker is not installed in this Windows environment; Tower Docker validation is required after push/deploy.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1 with GEOINTEL_INSTALL_AI=true passed after the AI runtime hardening and redeployed Tower from commit 9cad8d4.
• Deploy-time live migration smoke passed on Tower; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1 and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for http://192.168.10.150:1202, API proxy and icon.
• Tower container check passed: torch imported as 2.12.1+cu130, torch.cuda.is_available() returned False, ultralytics imported as 8.4.87, and scripts/yolo_preflight.py --enabled --json returned dependencies_available=true, status=not_configured, will_download_models=false, will_run_inference=false because no local model path is configured yet.
• Tower runtime YOLO_CONFIG_DIR is /app/storage/ultralytics; the directory exists, is writable and Ultralytics writes settings there instead of root config.
• Internal browser validation passed against http://192.168.10.150:1202: the live shell and Map workspace rendered without console errors, with database layer selection, Operational GIS controls, and both full-run modes visible.
• RED: python -m pytest backend\tests\test_sprint13_yolo_preflight.py -q failed before runtime diagnostics were implemented because runtime was absent from preflight output.
• python -m pytest backend\tests\test_sprint13_yolo_preflight.py -q passed: 8 tests.
• python -m pytest backend\tests\test_sprint13_yolo_preflight.py backend\tests\test_sprint8b_yolo_foundation.py backend\tests\test_docker_runtime_config.py -q passed: 39 tests.
• python -m compileall backend/app passed.
• cd backend && python -m pytest -q passed: 369 tests.
• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• bash scripts/run_readiness_check.sh passed: 369 backend tests plus frontend typecheck/build and Alembic head.
• cd backend && python -m alembic heads passed: 202606120900 (head).
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh, bash -n scripts/deploy_tower.sh, bash -n deploy/unraid/run-dockerman-container.sh and bash -n deploy/unraid/all-in-one-start.sh passed.
• Local Codex host still cannot run docker compose config because Docker is not installed in this Windows environment; Tower Docker validation is required after push/deploy.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1 with GEOINTEL_INSTALL_AI=true passed and redeployed Tower from commit 7a29e78.
• Deploy-time live migration smoke passed on Tower; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1 and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for http://192.168.10.150:1202, API proxy and icon.
• Tower container check passed: remote checkout is 7a29e78, geointel is healthy on 0.0.0.0:1202->80/tcp, and scripts/yolo_preflight.py --enabled --json reports dependencies_available=true, torch_version=2.12.1, ultralytics_version=8.4.88, cuda_available=false, yolo_config_dir=/app/storage/ultralytics, status=not_configured, will_download_models=false and will_run_inference=false.
• RED: python -m pytest backend\tests\test_sprint13_yolo_preflight.py::test_yolo_preflight_api_returns_canonical_envelope backend\tests\test_sprint118_yolo_preflight_ui.py -q failed before implementation because /api/v1/detection/yolo/preflight returned 404 and the Detection Lab did not surface a YOLO runtime preflight panel.
• python -m pytest backend\tests\test_sprint13_yolo_preflight.py::test_yolo_preflight_api_returns_canonical_envelope backend\tests\test_sprint118_yolo_preflight_ui.py -q passed: 2 tests.
• cd frontend && npm run typecheck passed after adding the preflight API client and Detection Lab panel.
• python -m pytest backend\tests\test_sprint13_yolo_preflight.py backend\tests\test_sprint118_yolo_preflight_ui.py backend\tests\test_sprint48_api_contract_audit.py -q passed: 13 tests.
• python -m compileall backend/app passed.
• cd backend && python -m pytest -q passed: 371 tests.
• cd frontend && npm run build passed.
• bash scripts/run_readiness_check.sh passed: 371 backend tests plus frontend typecheck/build and API contract audit for 80 documented routes.
• cd backend && python -m alembic heads passed: 202606120900 (head).
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh and bash -n scripts/deploy_tower.sh passed.
• git push passed after Tower/Gitea became reachable again and pushed commit 7aa9382.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1 with GEOINTEL_INSTALL_AI=true passed and redeployed Tower from commit 7aa9382.
• Deploy-time live migration smoke passed on Tower; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1 and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for http://192.168.10.150:1202, API proxy and icon.
• Live API check passed for GET http://192.168.10.150:1202/api/v1/detection/yolo/preflight: it returned the canonical data envelope, status=not_configured, YOLO_ENABLED=false, torch_version=2.12.1, ultralytics_version=8.4.89, yolo_config_dir=/app/storage/ultralytics, will_download_models=false and will_run_inference=false.
• Tower container check passed with explicit CLI --enabled: dependencies_available=true, torch_version=2.12.1, ultralytics_version=8.4.89, cuda_available=false, status=not_configured, will_download_models=false and will_run_inference=false because no local model path is configured yet.

Limitations:

• GEOINTEL_INSTALL_AI=true installs optional PyTorch/Ultralytics dependencies but still requires a user-provided local model file; GeoIntel does not download weights.
• Reuse mode intentionally reuses only the latest saved map-selection dataset for QA/QC. It does not delete or mutate older derived datasets/exports.

Next recommended pass:

• Run full readiness, deploy Tower, and browser-verify both Map run modes plus configured-YOLO preflight status in the live container.

Sprint 116 Operational GIS map workflow (2026-07-04)

Changed:

• Switched the default MapLibre basemap to an OpenStreetMap road raster style with visible attribution while preserving VITE_MAP_STYLE_URL as the override for managed/production map styles.
• Added a persisted database layer selector to the Map workspace so ready vector datasets can be opened directly from stored project data.
• Added an Operational GIS run panel that reuses the selected AOI bbox or active layer bbox and calls the existing persisted vector_features bbox selection workflow.
• Added a visible basemap policy notice when the public OpenStreetMap fallback is active.
• Added a guided operational workflow that brings query, derived dataset save, GeoJSON export, reference selection, QA/QC run and evidence handoff into the Map workspace.
• Added a one-click full GIS workflow action that runs persisted selection, saves the derived dataset, saves a GeoJSON export and optionally runs QA/QC against the selected reference dataset.
• Updated .env.example, CHANGELOG.md, docs/TODO.md, docs/ENVIRONMENT_SPEC.md and frontend/README.md.
• Added regression coverage in backend/tests/test_sprint116_operational_gis_map_workflow.py.

Validation:

• cd frontend && npm run typecheck passed.
• python -m pytest backend\tests\test_sprint116_operational_gis_map_workflow.py backend\tests\test_sprint85_map_workspace_density.py backend\tests\test_sprint106_map_bbox_extract.py backend\tests\test_sprint107_map_selection_export.py backend\tests\test_sprint108_map_selection_derived_dataset.py -q passed: 18 tests.
• python -m pytest backend\tests\test_sprint116_operational_gis_map_workflow.py backend\tests\test_sprint109_map_selection_qa_shortcut.py backend\tests\test_sprint107_map_selection_export.py backend\tests\test_sprint108_map_selection_derived_dataset.py -q passed: 11 tests.
• python -m pytest backend\tests\test_sprint116_operational_gis_map_workflow.py -q passed: 2 tests after aligning .env.example with the managed-style override policy.
• RED: python -m pytest backend\tests\test_sprint116_operational_gis_map_workflow.py -q failed before implementation because Run full GIS workflow, runFullGisWorkflow and fullWorkflowStatus were not present.
• python -m pytest backend\tests\test_sprint116_operational_gis_map_workflow.py -q passed: 2 tests after adding the full GIS workflow action.
• python -m pytest backend\tests\test_sprint116_operational_gis_map_workflow.py backend\tests\test_sprint106_map_bbox_extract.py backend\tests\test_sprint107_map_selection_export.py backend\tests\test_sprint108_map_selection_derived_dataset.py backend\tests\test_sprint109_map_selection_qa_shortcut.py backend\tests\test_sprint110_map_qa_evidence_drilldown.py -q passed: 18 tests.
• cd frontend && npm run build passed.
• python -m compileall backend/app passed.
• cd backend && python -m pytest -q passed: 364 tests.
• bash scripts/run_readiness_check.sh passed: 364 backend tests plus frontend typecheck/build.
• cd backend && python -m alembic heads passed: 202606120900 (head).
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1 passed; rebuilt and redeployed the all-in-one container on http://192.168.10.150:1202 from commit 350d50a.
• Deploy-time live migration smoke passed after the database became ready on attempt 3; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1 and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for frontend, API proxy and icon.
• Internal Codex browser validation passed against http://192.168.10.150:1202: the Map workspace rendered a nonblank road basemap, persisted vector layer selector, operational GIS run panel and bbox query action with no console errors.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1 passed again after the guided workflow update; rebuilt and redeployed the all-in-one container on http://192.168.10.150:1202 from commit 14b66e6.
• Deploy-time live migration smoke passed after the database became ready on attempt 2; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1 and Alembic head was 202606120900.
• Internal Codex browser validation passed: the Map workspace showed the basemap policy notice, database layer selector, guided operational GIS steps/actions and nonblank map canvas with no console errors.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1 passed after aligning .env.example; rebuilt and redeployed the all-in-one container on http://192.168.10.150:1202 from commit fc88ceb.
• Deploy-time live migration smoke passed after the database became ready on attempt 2; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1 and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for frontend, API proxy and icon.
• Tower runtime check passed after the one-click GIS workflow update: remote checkout is commit 5331358, the geointel all-in-one container is healthy and published on 0.0.0.0:1202->80/tcp.
• LIVE_SMOKE_CONTAINER=geointel bash scripts/live_migration_smoke.sh passed on Tower; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1, required runtime schema objects were present and Alembic head was 202606120900.
• bash scripts/verify_browser_runtime.sh http://192.168.10.150:1202 passed on Tower for frontend, API proxy and icon.
• Internal Codex browser validation passed against http://192.168.10.150:1202: the Map workspace rendered without console warnings/errors or horizontal overflow, the Run full GIS workflow action was present, and a live run produced the persisted query/save/export status Dataset/export complete. Select a reference dataset to add QA/QC.

Limitations:

• The default OpenStreetMap tile service is appropriate for local V1 testing and demos, but production or heavier deployments should set VITE_MAP_STYLE_URL to a managed tile/style provider.
• This pass does not add live GRB/OSM fetching, new API routes, new migrations or fake data.
• The one-click workflow can still create a new derived dataset/export each time it is clicked; a future polish pass should add explicit reuse/replace behavior for repeated operator runs.

Next recommended pass:

• Add reuse/replace behavior for repeated map workflow runs and make reference selection for QA/QC more guided in the Map workspace.

Sprint 115 QA/QC and Exports usability layout pass (2026-07-04)

Changed:

• Added a QA/Exports usability layer to the frontend shell CSS to reduce evidence/history density without changing behavior.
• Rebalanced QA/QC and Exports workspace columns for review-first usage.
• Made QA/QC summary, handoff, drilldown, feature evidence, metric history and raw provenance surfaces more compact.
• Reduced raw QA provenance height so JSON evidence remains available but no longer dominates the page.
• Made export handoff cards, latest-artifact cards, action cards and export history controls denser and easier to scan.
• Updated CHANGELOG.md, docs/TODO.md and frontend/README.md.
• Added regression coverage in backend/tests/test_sprint115_quality_export_usability_layout.py.

Validation:

• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• python -m pytest backend\tests\test_sprint115_quality_export_usability_layout.py backend\tests\test_sprint86_quality_workspace_density.py backend\tests\test_sprint89_export_system_density.py backend\tests\test_sprint70_quality_handoff_polish.py backend\tests\test_sprint78_export_preview_readability.py -q passed: 15 tests.
• python -m compileall backend/app passed.
• cd backend && python -m pytest -q passed: 362 tests.
• bash scripts/run_readiness_check.sh passed: 362 backend tests plus frontend typecheck/build.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1 passed; rebuilt and redeployed the all-in-one container on http://192.168.10.150:1202 from commit 915b34c.
• Deploy-time live migration smoke passed after the database became ready on attempt 2; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1 and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for frontend, API proxy and icon.

Limitations:

• This pass is visual/layout only; it does not change API contracts, persistence, export generation, QA metrics or map behavior.
• Live Tower redeploy has been run; the QA/QC and Exports usability layout pass is available at http://192.168.10.150:1202.

Next recommended pass:

• Run a live visual audit after redeploy, then refine AI Labs/System or address any remaining visual rough edges found in the browser.

Sprint 114 Data and Map usability layout pass (2026-07-04)

Changed:

• Added a Data/Map usability layer to the frontend shell CSS to improve the core select-and-extract workflow without changing behavior.
• Rebalanced the Data workspace columns and made dataset upload, role summaries, catalog cards, metrics and actions more compact.
• Made the Map workspace more map-first by ordering the MapLibre frame before dense controls and increasing desktop map height.
• Compressed map context, provenance, layer controls, bbox selection, selected-feature extraction and inspector surfaces.
• Updated CHANGELOG.md, docs/TODO.md and frontend/README.md.
• Added regression coverage in backend/tests/test_sprint114_data_map_usability_layout.py.

Validation:

• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• python -m pytest backend\tests\test_sprint113_calm_workbench_layout.py backend\tests\test_sprint114_data_map_usability_layout.py backend\tests\test_sprint74_data_map_mobile_polish.py backend\tests\test_sprint85_map_workspace_density.py -q passed: 10 tests.
• python -m compileall backend/app passed.
• cd backend && python -m pytest -q passed: 360 tests.
• bash scripts/run_readiness_check.sh passed: 360 backend tests plus frontend typecheck/build.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1 passed; rebuilt and redeployed the all-in-one container on http://192.168.10.150:1202 from commit 9721937.
• Deploy-time live migration smoke passed after the database became ready on attempt 2; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1 and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for frontend, API proxy and icon.

Limitations:

• This pass is visual/layout only; it does not change API contracts, persistence, map query behavior, exports or QA logic.
• Live Tower redeploy has been run; the Data and Map usability layout pass is available at http://192.168.10.150:1202.

Next recommended pass:

• Run a live visual audit after redeploy and then refine QA/QC and Exports in the same quieter style.

Sprint 113 calm workbench layout pass (2026-07-04)

Changed:

• Added a calm-density layer to the frontend shell CSS to reduce visual pressure without changing workflows.
• Softened the base palette, borders and shadows.
• Made the top context bar, left workspace navigation, main heading, readiness tiles and inspector surfaces more compact.
• Hid the duplicated workspace command bar because the sidebar remains the primary persistent navigation.
• Updated CHANGELOG.md, docs/TODO.md and frontend/README.md.
• Added regression coverage in backend/tests/test_sprint113_calm_workbench_layout.py.

Validation:

• cd frontend && npm run typecheck passed.
• python -m pytest backend\tests\test_sprint49_workbench_shell_refactor.py backend\tests\test_sprint62_frontend_visual_polish.py backend\tests\test_sprint82_shell_density_polish.py backend\tests\test_sprint83_workspace_panel_hierarchy.py -q passed: 10 tests.
• cd frontend && npm run build passed.
• python -m pytest backend\tests\test_sprint113_calm_workbench_layout.py -q passed: 2 tests.
• python -m compileall backend/app passed.
• cd backend && python -m pytest -q passed: 358 tests.
• bash scripts/run_readiness_check.sh passed: 358 backend tests plus frontend typecheck/build.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1 passed; rebuilt and redeployed the all-in-one container on http://192.168.10.150:1202 from commit 0355a3d.
• Deploy-time live migration smoke passed after the database became ready on attempt 3; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1 and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for frontend, API proxy and icon.

Limitations:

• This pass improves visual density and hierarchy only; no API, persistence, workflow or map behavior changed.
• Live Tower redeploy has been run; the calmer layout is available at http://192.168.10.150:1202.

Next recommended pass:

• Do a live visual audit after redeploy and then tune individual workspaces, starting with Data and Map, based on actual screenshots.

Sprint 112 QA evidence map overlay (2026-06-25)

Changed:

• Added QualityEvidenceService to resolve persisted QA/QC evidence ids back to stored geometries.
• Added GET /api/v1/projects/{project_id}/quality-checks/{quality_check_id}/evidence/geojson.
• The endpoint returns a canonical envelope with quality_check_id, dataset/run provenance, warnings and a GeoJSON FeatureCollection.
• Evidence resolution supports candidate dataset vector_features, candidate persisted detections/segmentations for analysis-run QA, and reference vector_features.
• Added QA/QC panel actions to show selected or latest evidence on the Map workspace.
• Added a MapLibre QA evidence source/layers with distinct match candidate, match reference, false-positive and false-negative styling.
• Added map overlay loading/error/clear state and a compact legend.
• Updated docs/API_CONTRACTS.md, frontend/README.md, CHANGELOG.md and docs/TODO.md.
• Added regression coverage in backend/tests/test_sprint112_qa_evidence_overlay.py.

Validation:

• RED: python -m pytest backend\tests\test_sprint112_qa_evidence_overlay.py -q failed before implementation because app.services.quality_evidence_service did not exist.
• RED: after backend implementation, the same test failed until frontend qaEvidenceData/API wiring existed.
• python -m pytest backend\tests\test_sprint112_qa_evidence_overlay.py -q passed: 4 tests.
• cd frontend && npm run typecheck passed after making the MapLibre expression type explicit.
• python -m pytest backend\tests\test_sprint112_qa_evidence_overlay.py backend\tests\test_qa_service.py backend\tests\test_sprint8c_detection_visualization_qa.py backend\tests\test_sprint9_segmentation_foundation.py -q passed: 25 tests.
• python -m compileall backend/app passed.
• cd backend && python -m pytest -q passed: 356 tests.
• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• cd backend && python -m alembic heads passed: 202606120900 (head).
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• bash scripts/run_readiness_check.sh passed: 356 backend tests plus frontend typecheck/build.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1 passed; rebuilt and redeployed the all-in-one container on http://192.168.10.150:1202 from commit b9674a0.
• Deploy-time live migration smoke passed against the container database after the database became ready on attempt 3; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1 and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for frontend, API proxy and icon.
• Live QA evidence API smoke passed: GET /api/v1/projects/c0b00f1f-80bf-4992-be94-f5e5e6f6bf63/quality-checks/2a291f88-5ed1-4110-ba6b-d200ec722098/evidence/geojson returned a GeoJSON FeatureCollection with 4 persisted evidence features and 0 warnings.
• Internal Codex browser validation passed against http://192.168.10.150:1202: the QA/QC evidence drilldown rendered, Show evidence overlay opened the Map workspace, MapLibre canvas was present, the QA/QC evidence overlay reported 4 rendered features, and the mobile viewport had no horizontal overflow.

Limitations:

• The overlay is generated read-only from existing persisted evidence and geometries; no new evidence table or migration was introduced.
• Missing evidence ids are reported as warnings and do not create fake geometries.
• No provider fetching, AI dependency, real model behavior or new product domain was added.

Next recommended pass:

• Consider a small export/handoff action for the evidence overlay GeoJSON or a reusable browser smoke script that verifies the QA evidence overlay after deploy.

Sprint 111 QA feature evidence persistence (2026-06-25)

Changed:

• Added feature-level evidence extraction to the shared QA IoU matcher.
• Dataset QA now returns and persists match_evidence, false_positive_evidence and false_negative_evidence.
• Detection QA and segmentation QA now use the same evidence-aware matcher and persist the same evidence keys in quality_checks.findings_json.
• Extended the QA/QC drilldown with compact matched, false-positive and false-negative feature id lists before the raw findings JSON.
• Updated docs/API_CONTRACTS.md, frontend/README.md, CHANGELOG.md and docs/TODO.md.
• Added/extended regression coverage in backend/tests/test_qa_service.py, backend/tests/test_sprint7a_persistence_foundation.py and backend/tests/test_sprint111_qa_feature_evidence.py.

Validation:

• RED: python -m pytest backend\tests\test_qa_service.py -q failed before implementation because QaProviderComparisonResult had no match_evidence.
• RED: python -m pytest backend\tests\test_sprint111_qa_feature_evidence.py -q failed before docs were updated because docs/API_CONTRACTS.md did not document the evidence keys.
• python -m pytest backend\tests\test_qa_service.py -q passed: 3 tests.
• python -m pytest backend\tests\test_sprint8c_detection_visualization_qa.py backend\tests\test_sprint9_segmentation_foundation.py -q passed: 18 tests.
• python -m pytest backend\tests\test_sprint111_qa_feature_evidence.py -q passed: 2 tests.
• python -m pytest backend\tests\test_qa_service.py backend\tests\test_sprint7a_persistence_foundation.py -q passed: 10 tests.
• python -m compileall backend/app passed.
• cd backend && python -m pytest -q passed: 352 tests.
• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• cd backend && python -m alembic heads passed: 202606120900 (head).
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• bash scripts/run_readiness_check.sh passed.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1 passed; rebuilt and redeployed the all-in-one container on http://192.168.10.150:1202.
• Deploy-time live migration smoke passed against the container database, including PostGIS/version/schema checks.
• Deploy-time browser runtime verification passed for frontend, API proxy and icon.
• Live QA API smoke passed: POST /api/v1/qa/detections-vs-reference returned match_evidence, false_positive_evidence and false_negative_evidence.
• Live persisted quality-check smoke passed: GET /api/v1/projects/c0b00f1f-80bf-4992-be94-f5e5e6f6bf63/quality-checks returned the evidence arrays in findings_json.

Limitations:

• Feature-level evidence is persisted as ids/IoU metadata in quality_checks.findings_json; no quality_check_items table or first-class evidence geometry table was introduced.
• Evidence map overlays can now be built from persisted ids, but overlay generation remains future work.
• No migration, provider fetching, AI dependency, real model behavior or new product domain was added.

Next recommended pass:

• Add QA evidence overlay generation by resolving persisted evidence ids back to candidate/reference geometries and rendering false positives/false negatives as MapLibre layers.

Sprint 110 Map QA evidence drilldown (2026-06-25)

Changed:

• Extended QaComparisonResult frontend typing with optional quality_check_id, matching the existing backend QA result payload.
• Updated useMapSelectionQa to keep the latest persisted map-selection quality-check id after a successful QA/QC comparison.
• Extended the Map workspace QA/QC result state with inline evidence: quality-check id, matches, false positives, false negatives, mean IoU and QA warnings.
• Added Open QA/QC evidence handoff from Map workspace to the existing QA/QC workspace drilldown, avoiding a parallel QA detail system.
• Added compact styling for the Map QA evidence and warning surface.
• Updated frontend/README.md, CHANGELOG.md and docs/TODO.md.
• Added regression coverage in backend/tests/test_sprint110_map_qa_evidence_drilldown.py.

Validation:

• RED: python -m pytest backend\tests\test_sprint110_map_qa_evidence_drilldown.py -q failed before implementation because the hook/evidence wiring was absent.
• python -m pytest backend\tests\test_sprint110_map_qa_evidence_drilldown.py -q passed: 2 tests.
• python -m compileall backend/app passed.
• cd backend && python -m pytest -q passed: 349 tests.
• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• cd backend && python -m alembic heads passed: 202606120900 (head).
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• bash scripts/run_readiness_check.sh passed.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1 passed; rebuilt and redeployed the all-in-one container on http://192.168.10.150:1202.
• Deploy-time live migration smoke passed against the container database, including PostGIS/version/schema checks.
• Deploy-time browser runtime verification passed for frontend, API proxy and icon.
• Live API proxy smoke passed: GET http://192.168.10.150:1202/api/v1/projects returned the demo project envelope.

Limitations:

• The Map evidence handoff opens the existing QA/QC workspace; selecting a specific historical check inside that workspace remains governed by the QA/QC panel's own latest-check behavior.
• False-positive and false-negative geometries are summarized by persisted metrics/findings; dedicated map overlays for unmatched evidence remain future work.
• No backend API contract, migration, provider fetching, AI dependency, real model behavior or new product domain was added.

Next recommended pass:

• Add optional unmatched-evidence map overlays once the QA persistence model stores explicit matched/unmatched feature ids or geometries.

Sprint 109 Map selection QA shortcut (2026-06-25)

Changed:

• Added useMapSelectionQa to keep Map workspace QA/QC orchestration out of App.tsx.
• Added a Map workspace QA/QC shortcut after Save as dataset, allowing the latest derived map selection dataset to be compared against a selected reference dataset.
• The shortcut reuses the existing qaApi.runQa flow and refreshes persisted quality checks/project data after completion.
• Added inline precision, recall, F1 and quality-check status feedback in the Map workspace.
• Added compact styling for the Map selection QA surface.
• Updated frontend/README.md, CHANGELOG.md and docs/TODO.md.
• Added regression coverage in backend/tests/test_sprint109_map_selection_qa_shortcut.py.

Validation:

• RED: python -m pytest backend\tests\test_sprint109_map_selection_qa_shortcut.py -q failed before implementation because the hook and wiring were absent.
• python -m pytest backend\tests\test_sprint109_map_selection_qa_shortcut.py -q passed: 2 tests.
• python -m compileall backend/app passed.
• cd backend && python -m pytest -q passed: 347 tests.
• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• cd backend && python -m alembic heads passed: 202606120900 (head).
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• bash scripts/run_readiness_check.sh passed.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1 passed; rebuilt and redeployed the all-in-one container on http://192.168.10.150:1202.
• Deploy-time live migration smoke passed against the container database, including PostGIS/version/schema checks.
• Deploy-time browser runtime verification passed for frontend, API proxy and icon.
• Live API QA smoke passed: POST /api/v1/qa/detections-vs-reference compared derived map selection dataset 266aa1de-908e-4bc4-987e-996374ad747b against reference dataset 21dd968a-2ba4-4798-89d4-f3af91f9d24c, returned precision=0.5, recall=0.5, f1_score=0.5, mean_iou=0.8339768339652592 and persisted quality check 1730c270-6dee-4000-8813-12aa91df6b33.
• Live quality-check list smoke passed: GET /api/v1/projects/c0b00f1f-80bf-4992-be94-f5e5e6f6bf63/quality-checks returned the persisted map-selection QA check with precision, recall, F1, mean IoU, false-positive and false-negative metrics.

Limitations:

• The Map shortcut currently uses the existing QA comparison defaults with IoU threshold 0.5.
• QA results are summarized inline; detailed false-positive/false-negative evidence remains in the QA/QC workspace.
• No backend API contract, migration, provider fetching, AI dependency, real model behavior or new product domain was added.

Next recommended pass:

• Add a QA result drilldown/handoff from the Map workspace so operators can inspect persisted false-positive and false-negative evidence directly after running selection QA/QC.

Sprint 108 Map selection derived datasets (2026-06-25)

Changed:

• Added POST /api/v1/projects/{project_id}/datasets/{dataset_id}/vector/select/derive.
• Added VectorSelectionDeriveRequest for bbox selection-to-derived-dataset requests.
• Added VectorOperationsService.derive_selection_dataset, which selects persisted PostGIS vector_features, writes a derived GeoJSON dataset artifact, stores source provenance and re-indexes the derived features into vector_features.
• Added Save as dataset to the Map workspace selection result state with loading/error/latest-dataset feedback.
• Updated frontend dataset API typing and wiring for selection-derived datasets.
• Updated docs/API_CONTRACTS.md, backend/README.md, frontend/README.md, CHANGELOG.md and docs/TODO.md.
• Added regression coverage in backend/tests/test_sprint108_map_selection_derived_dataset.py.

Validation:

• RED: python -m pytest backend\tests\test_sprint108_map_selection_derived_dataset.py -q failed before implementation because the service, route and frontend contracts were absent.
• python -m pytest backend\tests\test_sprint108_map_selection_derived_dataset.py -q passed: 4 tests.
• python -m compileall backend/app passed.
• cd backend && python -m pytest -q passed: 345 tests.
• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• cd backend && python -m alembic heads passed: 202606120900 (head).
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• bash scripts/run_readiness_check.sh passed.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1 passed; rebuilt and redeployed the all-in-one container on http://192.168.10.150:1202.
• Deploy-time live migration smoke passed against the container database, including PostGIS/version/schema checks.
• Deploy-time browser runtime verification passed for frontend, API proxy and icon.
• Live API smoke passed: POST /api/v1/projects/c0b00f1f-80bf-4992-be94-f5e5e6f6bf63/datasets/6e5a3926-1a15-4136-b54f-ba5de9ec0e84/vector/select/derive created derived dataset 266aa1de-908e-4bc4-987e-996374ad747b with dataset_role="derived", source_name="map_selection" and 2 features.
• Live API content smoke passed: GET /api/v1/projects/c0b00f1f-80bf-4992-be94-f5e5e6f6bf63/datasets/266aa1de-908e-4bc4-987e-996374ad747b/content returned a GeoJSON FeatureCollection with 2 features.
• Live PostGIS query smoke passed: POST /api/v1/projects/c0b00f1f-80bf-4992-be94-f5e5e6f6bf63/datasets/266aa1de-908e-4bc4-987e-996374ad747b/vector/select returned 2 features from the derived dataset's persisted vector_features.

Limitations:

• Selection-derived datasets are bbox-only and use EPSG:4326 coordinates.
• Empty selections are rejected with VECTOR_OPERATION_EMPTY_RESULT.
• No migrations, live provider fetching, AI dependency, real model behavior or new product domain were added.

Next recommended pass:

• Add an explicit QA/QC shortcut from a selected derived map dataset to compare it against a reference dataset without leaving the Map workspace.

Sprint 107 Map selection export handoff (2026-06-25)

Changed:

• Added vector_selection to the GeoJSON export contract.
• Added ExportService.export_vector_selection_geojson, which queries persisted PostGIS vector_features through VectorFeatureService.select_features_by_bbox, writes the selected FeatureCollection and persists an exports row with export_type="vector_selection_geojson".
• Extended POST /api/v1/exports/geojson to accept export_kind="vector_selection" with EPSG:4326 bbox and feature limit.
• Added frontend export API typing for bbox/limit and useExportWorkflow.exportMapSelectionGeoJson.
• Added Save area export to the Map workspace selection result state with loading/error/latest-path feedback.
• Updated docs/API_CONTRACTS.md, backend/README.md, frontend/README.md, CHANGELOG.md and docs/TODO.md.
• Added regression coverage in backend/tests/test_sprint107_map_selection_export.py.

Validation:

• RED: python -m pytest backend\tests\test_sprint107_map_selection_export.py -q failed before implementation because the selection export service, route contract and frontend wiring were absent.
• python -m pytest backend\tests\test_sprint107_map_selection_export.py -q passed: 3 tests.
• python -m compileall backend/app passed.
• cd backend && python -m pytest -q passed: 341 tests.
• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• cd backend && python -m alembic heads passed: 202606120900 (head).
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• bash scripts/run_readiness_check.sh passed.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1 passed; rebuilt and redeployed the all-in-one container on http://192.168.10.150:1202.
• Deploy-time live migration smoke passed against the container database, including PostGIS/version/schema checks.
• Deploy-time browser runtime verification passed for frontend, API proxy and icon.
• Live API smoke passed: POST /api/v1/exports/geojson with export_kind="vector_selection" created export 1d16c78b-7e8c-4081-8edc-5be38811b50e with export_type="vector_selection_geojson", feature_count=2 and source_table="vector_features".
• Live API content smoke passed: GET /api/v1/exports/1d16c78b-7e8c-4081-8edc-5be38811b50e/content returned a GeoJSON FeatureCollection with 2 persisted vector features.

Limitations:

• Selection exports are bbox-only and reuse the same EPSG:4326 constraints as the Map area selection endpoint.
• Saving a selection creates an export artifact, not a derived dataset.
• No migrations, live provider fetching, AI dependency, real model behavior or new product domain were added.

Next recommended pass:

• Add a live browser smoke that exercises Save area export, confirms the export appears in Export Center history and previews/downloads the persisted vector_selection_geojson artifact.

Sprint 106 Map area selection extract (2026-06-25)

Changed:

• Added POST /api/v1/projects/{project_id}/datasets/{dataset_id}/vector/select for read-only bbox selection over persisted PostGIS vector_features.
• Added VectorSelectionBBox, VectorSelectionRequest and VectorSelectionResponse schemas and exported them through the backend schema module.
• Added VectorFeatureService.select_features_by_bbox, including EPSG:4326 bbox validation, feature limit capping, PostGIS ST_Intersects query and GeoJSON FeatureCollection conversion from persisted geometries.
• Added frontend selectVectorFeatures API client support and useMapSelectionExtract.
• Extended the Map workspace with an Area selection panel, two-click map bbox selection, manual bbox inputs, selected-feature/AOI/active-layer bbox shortcuts, area GeoJSON download/copy actions and a compact selected-feature table.
• Extended GeoMap with selection-bbox and selection-result MapLibre GeoJSON overlays.
• Updated docs/API_CONTRACTS.md, frontend/README.md, backend/README.md, CHANGELOG.md and docs/TODO.md.
• Added regression coverage in backend/tests/test_sprint106_map_bbox_extract.py.

Validation:

• RED: python -m pytest backend\tests\test_sprint106_map_bbox_extract.py -q failed before implementation because the vector selection service, route and frontend contracts were absent.
• python -m pytest backend\tests\test_sprint106_map_bbox_extract.py -q passed: 5 tests.
• python -m compileall backend/app passed.
• cd backend && python -m pytest -q passed: 338 tests.
• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• bash scripts/run_readiness_check.sh passed.
• cd backend && python -m alembic heads passed: 202606120900 (head).
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1 passed and redeployed commit 851d722 to Tower on port 1202.
• Deploy-time live migration smoke passed after database readiness attempt 3; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1, required runtime schema objects were present and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for frontend, API proxy and icon.
• bash scripts/verify_workbench_default_state.sh http://192.168.10.150:1202 passed with demo project, AOI, 3/3 ready datasets and seeded QA/QC.
• Live API smoke passed against POST /api/v1/projects/{project_id}/datasets/{dataset_id}/vector/select: demo vector dataset returned 2 persisted features as a GeoJSON FeatureCollection.
• Internal browser validation passed against http://192.168.10.150:1202: Map workspace rendered Area selection, Start map bbox and Run area extract; Use layer bbox filled EPSG:4326 bbox values; Run area extract returned 2 features, showed vector_features source plus Download area GeoJSON and Copy area GeoJSON; console warnings/errors were empty.

Limitations:

• Selection shape is bbox-only in EPSG:4326. Polygon/lasso selection remains future work.
• The endpoint is read-only and does not create exports or derived datasets; operators can download the selected GeoJSON client-side.
• No migrations, live provider fetching, AI dependency, real model behavior or new product domain were added.

Next recommended pass:

• Add a browser/live smoke around the area selection panel after deploy, then consider export-center handoff for persisted selection artifacts if V1 needs server-side audit retention.

Sprint 105 Map feature extract (2026-06-25)

Changed:

• Added a Selection & extract surface to frontend/src/components/map/MapWorkspace.tsx.
• Clicking a visible map feature now gives operators a focused extraction panel with geometry type, coordinate count, EPSG:4326 bbox, property count and property table.
• Added client-side Download selected GeoJSON, Copy selected properties and Clear selection actions for the clicked feature.
• Added a dedicated selected-feature MapLibre source with fill/line/circle highlight layers in frontend/src/components/GeoMap.tsx.
• Wired the selected feature highlight through frontend/src/App.tsx.
• Added responsive selection/extract CSS in frontend/src/styles/app.css.
• Updated frontend/README.md, CHANGELOG.md and docs/TODO.md.
• Added backend/tests/test_sprint105_map_feature_extract.py.

Validation:

• RED: python -m pytest backend\tests\test_sprint105_map_feature_extract.py -q failed before implementation because the extract panel, selected-feature highlight layer and CSS contracts were absent.
• python -m pytest backend\tests\test_sprint105_map_feature_extract.py backend\tests\test_sprint19_map_workbench.py backend\tests\test_sprint85_map_workspace_density.py -q passed: 9 tests.
• python -m compileall backend/app passed.
• cd backend && python -m pytest -q passed: 333 tests.
• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• bash scripts/run_readiness_check.sh passed.
• cd backend && python -m alembic heads passed: 202606120900 (head).
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1 passed and redeployed commit 561304c to Tower on port 1202.
• Deploy-time live migration smoke passed after database readiness attempt 3; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1, required runtime schema objects were present and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for frontend, API proxy and icon.
• Internal browser live check confirmed the Map workspace renders the Selection & extract panel and empty selection guidance at http://192.168.10.150:1202.
• Live frontend bundle check confirmed Selection & extract, selected-feature and Download selected GeoJSON markers in /assets/index-DVYT-JPu.js.

Limitations:

• This pass extracts the single currently clicked and loaded map feature only.
• Rectangle, lasso or polygon selection over persisted PostGIS vector_features still requires a backend spatial query endpoint and drawing workflow.
• Internal browser canvas clicks did not reliably hit the small demo polygon features during validation; static regression tests, TypeScript build and live bundle markers verify the MapLibre selection wiring, and the live UI panel is present.
• No backend API contract, migration, provider fetching, AI dependency or persistence behavior changed.

Next recommended pass:

• Add map area/rectangle selection backed by a PostGIS spatial-query endpoint for multi-feature extraction, then expose export handoff for the selected result set.

Sprint 100 raster tile Segmentation Lab handoff (2026-06-24)

Changed:

• Added segmentationTileManifestPath state to frontend/src/hooks/useSegmentationWorkflow.ts.
• Sent tile_manifest_path in existing segmentation run requests when the field is populated.
• Added a Tile manifest input to frontend/src/components/segmentation/SegmentationLab.tsx.
• Added a Use in Segmentation Lab handoff beside the existing Detection Lab handoff in frontend/src/components/datasets/RasterControls.tsx.
• Wired the handoff through frontend/src/components/datasets/DatasetDetailPanel.tsx and frontend/src/App.tsx.
• Updated frontend/README.md and CHANGELOG.md.
• Added backend/tests/test_sprint100_segmentation_manifest_handoff.py.

Validation:

• RED: python -m pytest backend\tests\test_sprint100_segmentation_manifest_handoff.py -q failed before implementation because segmentation manifest state and handoff wiring did not exist.
• python -m pytest backend\tests\test_sprint100_segmentation_manifest_handoff.py -q passed.
• cd frontend && npm run typecheck passed.
• python -m pytest backend\tests\test_sprint100_segmentation_manifest_handoff.py backend\tests\test_sprint99_raster_ui_handoff.py backend\tests\test_sprint88_ai_lab_density.py backend\tests\test_sprint9_segmentation_foundation.py -q passed: 17 tests.
• cd frontend && npm run build passed.
• python -m compileall backend/app passed.
• python -m pytest -q from backend/ passed: 323 tests.
• bash scripts/run_readiness_check.sh passed.
• cd backend && python -m alembic heads passed: 202606120900 (head).
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1 passed and redeployed 140314a to Tower on port 1202.
• Deploy-time live migration smoke passed after the database became ready on attempt 3; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1, required runtime schema objects were present and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for frontend, API proxy and icon.
• bash scripts/verify_demo_raster_workflow.sh http://192.168.10.150:1202 passed: inspect, preview, stats and raster.tile.
• bash scripts/verify_workbench_default_state.sh http://192.168.10.150:1202 passed with 3/3 ready datasets.
• bash scripts/verify_workbench_interactions.sh http://192.168.10.150:1202 passed.
• Live frontend bundle check confirmed Latest tile manifest, Use in Detection Lab, Use in Segmentation Lab and Raster tile manifest path markers in /assets/index-DygxAZpB.js.

Limitations:

• Frontend handoff only; no backend route, migration, provider fetching, AI dependency or model behavior changes.
• Segmentation remains governed by the existing model registry and explicit-run controls.

Next recommended pass:

• Add a real browser interaction smoke for clicking both raster manifest handoff buttons into AI Labs, then continue with small usability polish around AI model readiness.

Sprint 99 raster tile Detection Lab handoff (2026-06-23)

Changed:

• Added latest raster tile manifest tracking to frontend/src/hooks/useDatasetWorkflow.ts from persisted raster.tile job results and direct tile generation responses.
• Surfaced the latest manifest path in frontend/src/components/datasets/RasterControls.tsx.
• Added a Use in Detection Lab handoff that fills the selected raster dataset and tile manifest path in the existing Detection Lab state.
• Updated frontend/src/components/datasets/DatasetDetailPanel.tsx, frontend/src/App.tsx, frontend/README.md and CHANGELOG.md.
• Added backend/tests/test_sprint99_raster_ui_handoff.py.

Validation:

• RED: python -m pytest backend\tests\test_sprint99_raster_ui_handoff.py -q failed before implementation because the raster tile manifest state and handoff wiring did not exist.
• python -m pytest backend\tests\test_sprint99_raster_ui_handoff.py -q passed.
• cd frontend && npm run typecheck passed.
• python -m pytest backend\tests\test_sprint99_raster_ui_handoff.py backend\tests\test_sprint95_raster_pipeline_hardening.py backend\tests\test_sprint28_dataset_workflow_hook.py -q passed: 6 tests.
• cd frontend && npm run build passed.
• python -m compileall backend/app passed.
• python -m pytest -q from backend/ passed: 322 tests.
• bash scripts/run_readiness_check.sh passed.
• cd backend && python -m alembic heads passed: 202606120900 (head).
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1 passed and redeployed aa41dfa to Tower on port 1202.
• Deploy-time live migration smoke passed after the database became ready on attempt 2; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1, required runtime schema objects were present and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for frontend, API proxy and icon.
• bash scripts/verify_demo_raster_workflow.sh http://192.168.10.150:1202 passed: inspect, preview, stats and raster.tile.
• bash scripts/verify_workbench_default_state.sh http://192.168.10.150:1202 passed with 3/3 ready datasets.
• bash scripts/verify_workbench_interactions.sh http://192.168.10.150:1202 passed.
• Live frontend bundle check confirmed Latest tile manifest and Use in Detection Lab markers in /assets/index-gJWV1CAi.js.

Limitations:

• Frontend handoff only; no API contract, migration, provider fetching, AI inference or model behavior changes.
• The Detection Lab still follows the existing model availability and explicit-run constraints.

Next recommended pass:

• Add a browser interaction smoke for clicking the raster manifest handoff into Detection Lab, or continue with segmentation tile-manifest prefill parity.

Sprint 98 demo raster workflow smoke (2026-06-23)

Changed:

• Added scripts/verify_demo_raster_workflow.sh, a browser-facing runtime smoke for the seeded demo_context_raster.tif fixture.
• Added the new smoke to readiness syntax checks.
• Hardened RasterOperationsService.tile so tile manifest bounds accept both Rasterio tuple bounds and object bounds.
• Updated backend/tests/test_raster_operations_service.py to cover tuple-based window bounds.
• Added backend/tests/test_sprint98_raster_workflow_smoke.py.
• Updated scripts/README.md and CHANGELOG.md.

Validation:

• RED: python -m pytest backend\tests\test_sprint98_raster_workflow_smoke.py -q failed before implementation because scripts/verify_demo_raster_workflow.sh did not exist.
• python -m pytest backend\tests\test_sprint98_raster_workflow_smoke.py backend\tests\test_readiness_gate.py -q passed: 14 tests.
• bash -n scripts/verify_demo_raster_workflow.sh passed.
• Live diagnostic run of bash scripts/verify_demo_raster_workflow.sh http://192.168.10.150:1202 showed inspect, preview and stats passed, then raster tile failed with HTTP 500.
• Tower backend logs identified the root cause: rasterio.windows.bounds(...) returned a tuple, while tile manifest generation expected .left/.bottom/.right/.top attributes.
• RED: python -m pytest backend\tests\test_raster_operations_service.py::test_raster_tile_returns_manifest_payload -q reproduced the live AttributeError after updating the fixture to tuple bounds.
• python -m pytest backend\tests\test_raster_operations_service.py::test_raster_tile_returns_manifest_payload backend\tests\test_sprint98_raster_workflow_smoke.py -q passed: 2 tests.
• python -m compileall backend/app passed.
• python -m pytest -q from backend/ passed: 321 tests.
• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• bash scripts/run_readiness_check.sh passed.
• cd backend && python -m alembic heads passed: 202606120900 (head).
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1 passed and redeployed 7b093c0 to Tower on port 1202.
• bash scripts/verify_demo_raster_workflow.sh http://192.168.10.150:1202 initially proved the tile fix but exposed the existing job terminal status convention as success rather than completed; the smoke now accepts success and completed.
• bash scripts/verify_demo_raster_workflow.sh http://192.168.10.150:1202 passed after the smoke status fix: inspect, preview, stats and raster tile manifest all succeeded.
• bash scripts/verify_workbench_default_state.sh http://192.168.10.150:1202 passed after redeploy.
• bash scripts/verify_workbench_interactions.sh http://192.168.10.150:1202 passed after redeploy.
• bash scripts/verify_demo_export_workflow.sh http://192.168.10.150:1202 passed after redeploy.
• bash scripts/run_readiness_check.sh passed again after the smoke status update.

Limitations:

• The raster smoke creates a small tile set each run. It is appropriate for local/runtime validation, not high-volume benchmarking.
• The smoke intentionally does not run AI inference or external imagery/provider fetching.

Next recommended pass:

• Run full readiness, redeploy, then verify the raster smoke live against http://192.168.10.150:1202.

Sprint 97 demo raster fixture workflow (2026-06-23)

Changed:

• Added raster_dataset_id to the demo workflow response contract.
• Extended DemoWorkflowService with a deterministic in-memory demo_context_raster.tif GeoTIFF fixture persisted through StorageService and the existing datasets table as a ready raster/fixture source dataset.
• Updated the frontend demo workflow hook so Detection and Segmentation Labs receive the seeded raster dataset while the candidate vector remains selected for Data, Map and Export review.
• Updated default-state and interaction smokes to require candidate vector, reference vector and raster fixture datasets as 3/3 ready.
• Updated scripts/README.md, frontend/README.md and CHANGELOG.md.

Validation:

• RED: python -m pytest backend\tests\test_sprint97_demo_raster_fixture.py -q failed before implementation because the demo schema/service and frontend hook did not expose or select a raster fixture.
• python -m pytest backend\tests\test_sprint97_demo_raster_fixture.py -q passed: 2 tests.
• python -m compileall backend/app passed.
• python -m pytest backend\tests\test_sprint97_demo_raster_fixture.py backend\tests\test_sprint15_demo_workflow.py backend\tests\test_sprint21_demo_workflow_smoke.py backend\tests\test_readiness_gate.py -q passed: 21 tests.
• bash -n scripts/verify_workbench_default_state.sh passed.
• bash -n scripts/verify_workbench_interactions.sh passed.
• cd frontend && npm run typecheck passed.
• python -m pytest backend\tests\test_sprint96_useful_default_context.py backend\tests\test_sprint39_frontend_orchestration_hooks.py -q passed: 11 tests.
• python -m pytest -q from backend/ passed: 320 tests.
• bash scripts/run_readiness_check.sh passed.
• cd frontend && npm run build passed.
• cd backend && python -m alembic heads passed: 202606120900 (head).
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1 passed and redeployed ca730ed to Tower on port 1202.
• bash scripts/verify_workbench_default_state.sh http://192.168.10.150:1202 passed and reported Datasets: 3/3 ready.
• bash scripts/verify_workbench_interactions.sh http://192.168.10.150:1202 passed and verified candidate, reference and raster fixture datasets.
• bash scripts/verify_demo_export_workflow.sh http://192.168.10.150:1202 passed.
• bash scripts/verify_gis_runtime.sh http://192.168.10.150:1202 passed.
• bash scripts/verify_browser_runtime.sh http://192.168.10.150:1202 passed.
• Live API check confirmed demo_context_raster.tif as raster/fixture/ready with EPSG:4326.
• Chrome headless screenshot of http://192.168.10.150:1202 showed the populated Overview workspace, selected demo context and no obvious desktop horizontal overflow.

Limitations:

• The raster fixture is a tiny generated local GeoTIFF for V1 workflow validation only. It does not represent external imagery and does not enable real AI inference.
• No migrations, provider fetching, real YOLO/SAM behavior or product scope beyond the existing offline demo workflow changed.
• Optional scripts/capture_workbench_screenshots.sh could not run in this Windows runner because Playwright is not installed; Chrome headless was used for the live visual check instead.

Next recommended pass:

• Continue with a focused raster operation happy-path smoke against the seeded raster fixture: inspect, preview, stats and tiling through the browser-facing API.

Sprint 48 Backend API contract audit (2026-06-17)

Changed:

• Added scripts/audit_api_contracts.py to import the FastAPI app, enumerate the implemented GET/POST/PATCH/DELETE route surface and compare it with active ### METHOD route headings in docs/API_CONTRACTS.md.
• Added the API contract audit to scripts/run_readiness_check.sh.
• Corrected contract drift in docs/API_CONTRACTS.md:
  • documented GET/PATCH /api/v1/projects/{project_id}/areas/{area_id};
  • corrected vector stats from POST to implemented GET;
  • documented GET /api/v1/projects/{project_id}/datasets/{dataset_id}/content;
  • changed non-implemented building-stats, legacy analysis object-detection/segmentation and YOLO export entries from active route headings to future-route notes.
• Added backend/tests/test_sprint48_api_contract_audit.py.
• Updated scripts/README.md, docs/TODO.md and CHANGELOG.md.

Validation:

• RED: cd backend && python -m pytest tests/test_sprint48_api_contract_audit.py -q failed before implementation because the audit script, readiness integration and route docs were missing.
• RED: python scripts/audit_api_contracts.py reported missing docs for 4 implemented routes and 5 stale documented routes.
• python scripts/audit_api_contracts.py passed: 76 implemented routes matched docs and 2 explicit non-envelope endpoints were tracked.
• cd backend && python -m pytest tests/test_sprint48_api_contract_audit.py tests/test_readiness_gate.py -q passed: 12 tests.
• python -m py_compile scripts/audit_api_contracts.py passed.
• python -m compileall backend/app passed.
• cd backend && python -m pytest -W error::DeprecationWarning passed: 201 tests.
• bash scripts/run_readiness_check.sh passed and included API contract audit OK.
• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• cd backend && python -m alembic heads passed: 202606120900 (head).
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1 passed and redeployed the all-in-one container to Tower on port 1202.
• bash scripts/verify_gis_runtime.sh http://192.168.10.150:1202 passed after redeploy.
• bash scripts/verify_workbench_default_state.sh http://192.168.10.150:1202 passed after redeploy.
• bash scripts/verify_workbench_interactions.sh http://192.168.10.150:1202 passed after redeploy.

Limitations:

• This pass audits route documentation presence, implemented/stale route drift and explicit non-envelope exceptions. It does not yet exercise every error path response body at runtime.
• No API behavior, migrations, provider fetching, AI behavior or product capabilities changed.

Next recommended pass:

• Add an error-envelope runtime audit for representative invalid/missing-resource paths across projects, datasets, providers, detection, segmentation, QA and exports.

Sprint 47 Workbench interaction smoke (2026-06-17)

Changed:

• Added stable data-testid anchors to the existing project, area, map, dataset, QA/QC and export controls so browser checks can target real controls instead of brittle text/layout selectors.
• Added scripts/verify_workbench_interactions.sh, a dependency-light runtime smoke that verifies the backing state for project switching, AOI/map selection, dataset selection, QA refresh and export refresh through the browser-facing API proxy.
• Added the script syntax check to scripts/run_readiness_check.sh.
• Added backend/tests/test_sprint47_workbench_interaction_smoke.py to keep the UI anchors, readiness gate and interaction smoke contract in place.
• Updated scripts/README.md, docs/TODO.md and CHANGELOG.md.

Validation:

• RED: cd backend && python -m pytest tests/test_sprint47_workbench_interaction_smoke.py -q failed before implementation because the UI anchors, readiness script reference and interaction smoke script were missing.
• cd backend && python -m pytest tests/test_sprint47_workbench_interaction_smoke.py -q passed: 3 tests.
• cd frontend && npm run typecheck passed.
• bash -n scripts/verify_workbench_interactions.sh passed.
• bash scripts/verify_workbench_interactions.sh http://192.168.10.150:1202 passed against the pre-deploy runtime API surface.
• python -m compileall backend/app passed.
• cd backend && python -m pytest -W error::DeprecationWarning passed: 198 tests.
• bash scripts/run_readiness_check.sh passed.
• cd frontend && npm run build passed.
• cd backend && python -m alembic heads passed: 202606120900 (head).
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1 passed and redeployed the all-in-one container to Tower on port 1202.
• bash scripts/verify_workbench_interactions.sh http://192.168.10.150:1202 passed after redeploy.
• bash scripts/verify_workbench_default_state.sh http://192.168.10.150:1202 passed after redeploy.
• bash scripts/verify_gis_runtime.sh http://192.168.10.150:1202 passed after redeploy.
• Live browser check against http://192.168.10.150:1202 passed using the new anchors: project, map, dataset, QA/QC and export panels were present; QA refresh and export metadata actions worked; latest export updated; no horizontal overflow was detected.

Limitations:

• The shell smoke validates the state behind the controls but does not click rendered controls by itself. The added data-testid anchors are intended for Codex/browser click checks and future browser artifact automation.
• No API contracts, migrations, provider fetching, AI behavior or product capabilities changed.

Next recommended pass:

• Add persisted screenshot artifact automation for the anchored browser pass, or move to a backend service contract audit if UI stabilization is sufficient.

Sprint 46 Workbench default-state smoke (2026-06-17)

Changed:

• Added scripts/verify_workbench_default_state.sh, a dependency-light runtime smoke for the browser-facing workbench default demo state.
• The smoke calls the offline demo workflow, then verifies GeoIntel Demo - Building QA, Demo AOI - Geel buildings, 2/2 ready datasets and a persisted QA/QC result through canonical data.items envelopes.
• Added the script syntax check to scripts/run_readiness_check.sh.
• Extended backend/tests/test_readiness_gate.py so the readiness gate and script keep covering the default-state smoke contract.
• Updated scripts/README.md and CHANGELOG.md.

Validation:

• RED: cd backend && python -m pytest tests/test_readiness_gate.py -q failed before implementation because verify_workbench_default_state.sh was missing and readiness did not reference it.
• cd backend && python -m pytest tests/test_readiness_gate.py -q passed: 9 tests.
• bash -n scripts/verify_workbench_default_state.sh passed.
• bash scripts/verify_workbench_default_state.sh http://192.168.10.150:1202 passed.
• python -m compileall backend/app passed.
• cd backend && python -m pytest -W error::DeprecationWarning passed: 195 tests.
• bash scripts/run_readiness_check.sh passed.
• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• cd backend && python -m alembic heads passed: 202606120900 (head).
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1 passed and redeployed the all-in-one container to Tower on port 1202.
• bash scripts/verify_workbench_default_state.sh http://192.168.10.150:1202 passed after redeploy.
• bash scripts/verify_gis_runtime.sh http://192.168.10.150:1202 passed after redeploy.
• bash scripts/verify_demo_export_workflow.sh http://192.168.10.150:1202 passed after redeploy.
• Live browser check against http://192.168.10.150:1202 passed at 1280x720: selected project GeoIntel Demo - Building QA, status showed 1 area, 2/2 ready datasets and 2 checks, map workspace rendered and no horizontal overflow was detected.

Limitations:

• The committed smoke validates the connected default workbench state through HTTP/API contracts, not pixel layout. Browser screenshot and overflow validation remain a Codex/browser verification step.
• No API contracts, migrations, provider fetching, AI behavior or product capabilities changed.

Next recommended pass:

• Add a deeper browser interaction smoke for core controls, starting with project switching, area selection and map layer opacity.

Sprint 45 Default demo selection polish (2026-06-17)

Changed:

• Updated frontend/src/hooks/useProjectWorkspace.ts so cold-start project selection preserves an existing selected project, honors an explicit preferred project and otherwise prefers a populated demo/workbench project over an empty first project.
• New project creation now selects the newly created project immediately after creation.
• Updated frontend/src/hooks/useDemoWorkflow.ts so demo seed refresh passes the seeded project id to loadProjects.
• Extended orchestration tests to cover preferred demo selection and project creation selection behavior.

Validation:

• cd backend && python -m pytest tests/test_sprint39_frontend_orchestration_hooks.py tests/test_sprint21_demo_workflow_smoke.py -q passed: 11 tests.
• python -m compileall backend/app passed.
• cd backend && python -m pytest -W error::DeprecationWarning passed: 194 tests.
• bash scripts/run_readiness_check.sh passed.
• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• cd backend && python -m alembic heads passed: 202606120900 (head).
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• Browser check against local Vite preview with live backend passed after async settle: selected project GeoIntel Demo - Building QA, status showed 1 area, 2/2 ready datasets and 2 checks.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1 passed and redeployed the all-in-one container to Tower on port 1202.
• bash scripts/verify_gis_runtime.sh http://192.168.10.150:1202 passed.
• bash scripts/verify_demo_export_workflow.sh http://192.168.10.150:1202 passed.
• Live browser check against http://192.168.10.150:1202 passed after async settle: selected project GeoIntel Demo - Building QA, status showed 1 area, 2/2 ready datasets and 2 checks, with no horizontal overflow.

Limitations:

• No API contracts, migrations, provider fetching, AI behavior or product capabilities changed.
• The heuristic probes up to eight project candidates on cold start; a future backend list endpoint with area/dataset counts would make this cleaner.

Next recommended pass:

• Add a lightweight browser regression script for the demo start page once the UI state is stable enough to automate end to end.

Sprint 44 Workbench UI polish pass (2026-06-17)

Changed:

• Reworked frontend/src/styles/app.css from a minimal browser-default stylesheet into a compact GIS workbench skin with modern controls, restrained neutral/green accents, scroll-contained long panels and responsive layout rules.
• Promoted MapWorkspace above the dense workflow grid in frontend/src/App.tsx so GIS context is visible before lower-detail provider, AI, QA and export panels.
• Moved DatasetPanel into the first workflow row beside project/area/provider setup.
• Added a static layout regression test in backend/tests/test_sprint30_workbench_components.py for map-first ordering and scroll-contained workflow panels.
• Updated frontend README and changelog.

Validation:

• Browser visual check against local Vite preview passed at desktop width: map-first layout visible, workflow panels scroll-contained, page height reduced from roughly 14.9k px to roughly 3.2k px.
• Browser responsive check at 390px width passed with no horizontal overflow.
• python -m compileall backend/app passed.
• cd backend && python -m pytest -W error::DeprecationWarning passed: 194 tests.
• bash scripts/run_readiness_check.sh passed.
• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• cd backend && python -m alembic heads passed: 202606120900 (head).
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1 passed and redeployed geointel-all-in-one:latest to Tower on port 1202.
• bash scripts/verify_gis_runtime.sh http://192.168.10.150:1202 passed.
• bash scripts/verify_demo_export_workflow.sh http://192.168.10.150:1202 passed.
• Live browser check against http://192.168.10.150:1202 passed: map-first layout rendered, scroll height roughly 3.2k px and no desktop horizontal overflow detected.

Limitations:

• No API contracts, migrations, provider fetching, AI behavior or product capabilities changed.
• This is still a dense operational workbench rather than a designed product shell with navigation or tabs.
• The live default selected project can still be an empty project with the demo name; project selection polish remains a separate follow-up.

Next recommended pass:

• Add UI browser regression coverage for the map-first demo workflow and polish the default project/demo selection state.

Sprint 43 Workbench bootstrap hook decomposition (2026-06-17)

Changed:

• Moved frontend bootstrap, project-change reload/reset and detection/segmentation result reload effects from frontend/src/App.tsx into frontend/src/hooks/useWorkbenchBootstrap.ts.
• Kept App.tsx as a composition root that wires hook outputs into panels; it no longer imports useEffect.
• Extended orchestration tests so lifecycle side effects stay in the bootstrap hook.
• Updated frontend README, changelog and TODO status.

Validation:

• cd backend && python -m pytest tests/test_sprint39_frontend_orchestration_hooks.py -q passed: 9 tests.
• python -m compileall backend/app passed.
• cd backend && python -m pytest -W error::DeprecationWarning passed: 193 tests.
• bash scripts/run_readiness_check.sh passed.
• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• cd backend && python -m alembic heads passed: 202606120900 (head).
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1 passed and redeployed geointel-all-in-one:latest to Tower on port 1202.
• bash scripts/verify_gis_runtime.sh http://192.168.10.150:1202 passed.
• bash scripts/verify_demo_export_workflow.sh http://192.168.10.150:1202 passed.
• App.tsx size audit after extraction: 621 lines; useWorkbenchBootstrap.ts: 81 lines.

Limitations:

• No UX behavior, API contracts, migrations, provider fetching or AI behavior changed.
• App.tsx line count remains high because it explicitly wires many panel props; the remaining size is primarily composition.

Next recommended pass:

• Pick the next V1 stabilization focus: UI browser regression coverage, backend service contract audit, or golden dataset expansion.

Sprint 42 App entrypoint cleanup (2026-06-17)

Changed:

• Removed the stale FormEvent/useState React imports from frontend/src/App.tsx.
• Removed the UTF-8 BOM from App.tsx so patches and static checks use normal UTF-8 text.
• Added a regression test that verifies the clean entrypoint encoding and React import set.
• Recorded the current App.tsx size audit: 622 lines after the orchestration hook decomposition passes.

Validation:

• cd backend && python -m pytest tests/test_sprint39_frontend_orchestration_hooks.py -q passed: 8 tests.
• python -m compileall backend/app passed.
• cd backend && python -m pytest -W error::DeprecationWarning passed: 192 tests.
• bash scripts/run_readiness_check.sh passed.
• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• cd backend && python -m alembic heads passed: 202606120900 (head).
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1 passed and redeployed geointel-all-in-one:latest to Tower on port 1202.
• bash scripts/verify_gis_runtime.sh http://192.168.10.150:1202 passed.
• bash scripts/verify_demo_export_workflow.sh http://192.168.10.150:1202 passed.

Limitations:

• No UX behavior, API contracts, migrations, provider fetching or AI behavior changed.
• App.tsx remains a large composition root; the remaining size is primarily panel wiring and hook outputs.

Next recommended pass:

• Optional bootstrap-effect extraction if another no-behavior size reduction is useful.

Sprint 41 Demo workflow hook decomposition (2026-06-17)

Changed:

• Moved offline demo workflow orchestration from frontend/src/App.tsx into frontend/src/hooks/useDemoWorkflow.ts.
• Preserved the existing backend fixture seed flow and all cross-module selection updates for project, candidate/reference datasets, map AOI, QA/QC, detection, segmentation and exports.
• Extended static frontend orchestration tests so demoApi.seedWorkflow is owned by the new hook and not App.tsx.
• Updated frontend README, changelog and TODO status.

Validation:

• cd backend && python -m pytest tests/test_sprint21_demo_workflow_smoke.py tests/test_sprint39_frontend_orchestration_hooks.py -q passed: 9 tests.
• python -m compileall backend/app passed.
• cd backend && python -m pytest -W error::DeprecationWarning passed: 191 tests.
• bash scripts/run_readiness_check.sh passed.
• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• cd backend && python -m alembic heads passed: 202606120900 (head).
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1 passed and redeployed geointel-all-in-one:latest to Tower on port 1202.
• bash scripts/verify_gis_runtime.sh http://192.168.10.150:1202 passed.
• bash scripts/verify_demo_export_workflow.sh http://192.168.10.150:1202 passed.

Limitations:

• No UX behavior, API contracts, migrations, provider fetching or AI behavior changed.
• App.tsx still has a UTF-8 BOM and a stale React import cleanup opportunity; TypeScript build is unaffected.

Next recommended pass:

• Do a final App.tsx size/import cleanup pass.

Sprint 40 Project workspace hook decomposition (2026-06-17)

Changed:

• Moved project list/create state, area create state and project-scoped area/dataset loading into frontend/src/hooks/useProjectWorkspace.ts.
• Kept demo workflow orchestration in App.tsx because it coordinates project, dataset, QA/QC, detection, segmentation and export selections across multiple hooks.
• Moved default clip-area fallback selection into frontend/src/hooks/useDatasetWorkflow.ts.
• Moved default map-area fallback selection into frontend/src/hooks/useMapWorkspaceState.ts.
• Extended static frontend orchestration tests to lock these ownership boundaries.
• Updated frontend README, changelog and TODO status.

Validation:

• cd backend && python -m pytest tests/test_sprint39_frontend_orchestration_hooks.py -q passed: 6 tests.
• python -m compileall backend/app passed.
• cd backend && python -m pytest -W error::DeprecationWarning passed: 190 tests.
• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• bash scripts/run_readiness_check.sh passed.
• cd backend && python -m alembic heads && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1 rebuilt and redeployed the all-in-one Tower container at http://192.168.10.150:1202.
• Tower live migration smoke passed and reported Database collation version: ok.
• Tower browser runtime verification passed.
• bash scripts/verify_gis_runtime.sh http://192.168.10.150:1202 passed.
• bash scripts/verify_demo_export_workflow.sh http://192.168.10.150:1202 passed.

Open:

• None for this pass.

Limitations:

• No UX behavior, API contracts, migrations, provider fetching or AI behavior changed.
• Demo workflow orchestration remains in App.tsx until it can be safely split without obscuring cross-module state updates.

Next recommended pass:

• Run full readiness and Tower deploy verification, then consider extracting the demo workflow coordinator into a dedicated hook.

Sprint 39 Frontend orchestration decomposition (2026-06-17)

Changed:

• Moved provider capability loading state and externalApi.listProviders calls into frontend/src/hooks/useProviderCapabilities.ts.
• Moved change-detection state, validation and analysisApi.runChangeDetection calls into frontend/src/hooks/useChangeDetectionWorkflow.ts.
• Moved map-layer derived state, area GeoJSON feature construction and selected-feature reset behavior into frontend/src/hooks/useMapWorkspaceState.ts.
• Kept App.tsx as the cross-module composition layer without changing panel props, API contracts, migrations or product behavior.
• Added static regression tests for the extracted orchestration hooks.
• Updated frontend README, changelog and TODO status.

Validation:

• cd backend && python -m pytest tests/test_sprint39_frontend_orchestration_hooks.py -q passed: 4 tests.
• python -m compileall backend/app passed.
• cd backend && python -m pytest -W error::DeprecationWarning passed: 188 tests.
• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• bash scripts/run_readiness_check.sh passed.
• cd backend && python -m alembic heads && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1 rebuilt and redeployed the all-in-one Tower container at http://192.168.10.150:1202.
• Tower live migration smoke passed and reported Database collation version: ok.
• Tower browser runtime verification passed.
• bash scripts/verify_gis_runtime.sh http://192.168.10.150:1202 passed.
• bash scripts/verify_demo_export_workflow.sh http://192.168.10.150:1202 passed.

Open:

• None for this pass.

Limitations:

• Project/area/dataset cross-load orchestration still lives in App.tsx; it is a good next low-risk decomposition target.

Next recommended pass:

• Run full readiness and Tower deploy verification, then extract project/area loading into a dedicated hook if behavior remains stable.

Sprint 38 Export Center preview hardening (2026-06-17)

Changed:

• Hardened the export content preview path so HTML report artifacts return EXPORT_CONTENT_UNSUPPORTED instead of a generic JSON parse failure.
• Updated the frontend Export Center to offer JSON preview only for JSON/GeoJSON artifacts.
• HTML project report artifacts now display as download-only in the export list.
• Extracted export preview rendering from frontend/src/App.tsx into frontend/src/components/exports/ExportPreview.tsx.
• Updated API/frontend docs, changelog and TODO status.

Validation:

• cd backend && python -m pytest tests/test_sprint17_export_foundation.py -q passed: 10 tests.
• cd frontend && npm run typecheck passed.
• python -m compileall backend/app passed.
• cd backend && python -m pytest -W error::DeprecationWarning passed: 184 tests.
• cd frontend && npm run build passed.
• bash scripts/run_readiness_check.sh passed.
• cd backend && python -m alembic heads && python -m alembic upgrade head --sql passed.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1 rebuilt and redeployed the all-in-one Tower container at http://192.168.10.150:1202.
• Tower live migration smoke passed and reported Database collation version: ok.
• bash scripts/verify_gis_runtime.sh http://192.168.10.150:1202 passed.
• bash scripts/verify_demo_export_workflow.sh http://192.168.10.150:1202 passed.

Open:

• None for this pass.

Limitations:

• This pass does not add new export formats, PDF reports, provider fetching, AI inference or migrations.

Next recommended pass:

• Run the full readiness gate, frontend build and Tower deploy smoke; then continue with shared workbench orchestration decomposition or export cleanup/history filtering.

Sprint 37 Tower PostgreSQL collation maintenance (2026-06-17)

Changed:

• Performed the manual PostgreSQL collation maintenance action reported by the live migration smoke on the Tower all-in-one runtime.
• Created a pre-maintenance custom-format database backup at backups/geointel-before-collation-refresh-20260617-065707.dump.
• Ran REINDEX DATABASE geointel; followed by ALTER DATABASE "geointel" REFRESH COLLATION VERSION;.
• Confirmed the database collation metadata now matches the runtime collation version: geointel stored=2.36 actual=2.36.

Validation:

• LIVE_SMOKE_CONTAINER=geointel bash scripts/live_migration_smoke.sh passed on Tower and reported Database collation version: ok.
• bash scripts/verify_browser_runtime.sh http://192.168.10.150:1202 passed.
• bash scripts/verify_gis_runtime.sh http://192.168.10.150:1202 passed.
• bash scripts/verify_demo_export_workflow.sh http://192.168.10.150:1202 passed.

Notes:

• An initial SSH script attempt was interrupted by PowerShell BOM/stdin handling before reindex or refresh executed; the subsequent base64-delivered script ran without docker exec -i stdin interference.
• Earlier backup attempts were left in backups/ alongside the final validated backup.
• No code, API contracts, migrations, product features, provider fetching or AI behavior changed.

Next recommended pass:

• Continue with export/download polish or frontend export center hardening, using the now-clean Tower runtime as the validation target.

Sprint 32 Unraid all-in-one runtime (2026-06-17)

Changed:

• Added docker-compose.unraid.yml for a single editable geointel container on Unraid.
• Added deploy/unraid/Dockerfile.all-in-one to build one image containing embedded PostGIS, backend GIS runtime, nginx and frontend static assets.
• Added deploy/unraid/all-in-one-start.sh to start embedded PostGIS, apply Alembic migrations, start FastAPI on internal localhost and serve nginx on container port 80.
• Added deploy/unraid/nginx-all-in-one.conf so /api and /health proxy to 127.0.0.1:8000 inside the same container.
• Updated deploy/unraid/geointel.env.example, Unraid XML template and README for one-container operation and editable web/storage/database paths.
• Updated Tower deploy scripts to stop the old multi-container stack without deleting volumes and start docker-compose.unraid.yml.
• Updated scripts/live_migration_smoke.sh to support both all-in-one geointel and legacy backend Compose services.
• Added root .dockerignore for all-in-one builds.
• Updated Sprint 31 tests to cover the all-in-one Dockerfile, startscript, nginx config, deploy scripts and template metadata.

Validation:

• python -m pytest backend/tests/test_sprint31_unraid_template.py backend/tests/test_live_migration_smoke_script.py backend/tests/test_docker_runtime_config.py passed: 26 tests.
• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• python -m compileall backend/app passed.
• cd backend && python -m pytest passed: 179 tests.
• bash scripts/run_readiness_check.sh passed: 179 backend tests, frontend typecheck/build, Alembic head check and script syntax checks.
• cd backend && python -m alembic heads passed: single head 202606120900.
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• bash -n deploy/unraid/all-in-one-start.sh passed.

Notes:

• No API contracts, migrations, product features, provider fetching or AI behavior changed.
• Local Windows environment does not have docker in PATH; Tower deployment should provide the live all-in-one Docker validation.

Sprint 31 Unraid deployment template (2026-06-17)

Changed:

• Made docker-compose.yml configurable through .env defaults for frontend port, backend port, storage path, PostGIS database/user/password, CORS origins and upload limit.
• Added deploy/unraid/geointel.env.example for Unraid/Tower runtime configuration.
• Added deploy/unraid/geointel-unraid-template.xml as Unraid/DockerMan-style metadata for the editable Compose stack settings.
• Added deploy/unraid/geointel-icon.svg and deploy/unraid/geointel-icon.png, served through matching frontend/public assets.
• Added the frontend favicon link for the GeoIntel icon.
• Added Sprint 31 tests for Unraid template coverage, compose variable coverage, docs and icon availability.
• Updated root README, TODO and changelog docs.

Validation:

• python -m pytest backend/tests/test_sprint31_unraid_template.py backend/tests/test_docker_runtime_config.py passed: 22 tests.
• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• python -m compileall backend/app passed.
• cd backend && python -m pytest passed: 177 tests.
• bash scripts/run_readiness_check.sh passed: 177 backend tests, frontend typecheck/build, Alembic head check and script syntax checks.
• cd backend && python -m alembic heads passed: single head 202606120900.
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• Tower deploy via scripts/deploy_tower.ps1 passed after commit 3a8b82f.
• Tower Docker Compose config/build/up passed with the env-default compose file.
• Tower live migration smoke passed with PostGIS 3.4 and Alembic head 202606120900.
• Tower browser runtime verification passed on http://192.168.10.150:1202.
• Additional HTTP smoke passed for frontend HTML, GET /api/v1/projects, /geointel-icon.svg and /geointel-icon.png.

Notes:

• Local Windows environment does not have docker in PATH, so local docker compose config could not be run from this machine.
• Tower deployment validated Docker Compose config and live runtime after commit.
• No API contracts, backend behavior, migrations, product features, provider fetching or AI behavior changed.

Sprint 30 workbench component decomposition (2026-06-17)

Changed:

• Moved persisted QA/QC result rendering from frontend/src/App.tsx into frontend/src/components/quality/QualityResultsPanel.tsx.
• Moved map layer controls, MapLibre composition and feature inspector rendering into frontend/src/components/map/MapWorkspace.tsx.
• Updated map/workbench and QA regression tests for the new component boundaries.
• Added Sprint 30 component wiring tests to keep QA and map markup out of App.tsx.
• Updated frontend README, TODO and changelog docs.

Validation:

• python -m pytest backend/tests/test_sprint30_workbench_components.py backend/tests/test_sprint27_frontend_workflow_hooks.py backend/tests/test_sprint19_map_workbench.py passed: 9 tests.
• python -m compileall backend/app passed.
• cd backend && python -m pytest passed: 173 tests.
• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• bash scripts/run_readiness_check.sh passed: 173 backend tests, frontend typecheck/build, Alembic head check and script syntax checks.
• cd backend && python -m alembic heads passed: single head 202606120900.
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• Tower deploy via scripts/deploy_tower.ps1 passed after commit acf9590.
• Tower live migration smoke passed with PostGIS 3.4 and Alembic head 202606120900.
• Tower browser runtime verification passed on http://192.168.10.150:1202.
• Additional HTTP smoke passed for frontend HTML and GET /api/v1/projects through the frontend proxy.

Notes:

• No API contracts, backend behavior, migrations, product features, provider fetching, AI behavior or UI redesign changed.
• Next maintainability pass should split export preview and remaining shared workbench orchestration into focused components/hooks.

Sprint 29 dataset component decomposition (2026-06-17)

Changed:

• Moved dataset upload/list rendering from frontend/src/App.tsx into frontend/src/components/datasets/DatasetPanel.tsx.
• Moved dataset detail and job-list rendering into frontend/src/components/datasets/DatasetDetailPanel.tsx.
• Split raster controls and vector controls into frontend/src/components/datasets/RasterControls.tsx and frontend/src/components/datasets/VectorControls.tsx.
• Updated Sprint 28 regression tests for the new component boundary and added Sprint 29 component wiring tests.
• Updated frontend README, TODO and changelog docs.

Validation:

• python -m pytest backend/tests/test_sprint29_dataset_components.py backend/tests/test_sprint28_dataset_workflow_hook.py backend/tests/test_sprint27_frontend_workflow_hooks.py passed: 10 tests.
• python -m compileall backend/app passed.
• cd backend && python -m pytest passed: 170 tests.
• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• bash scripts/run_readiness_check.sh passed: 170 backend tests, frontend typecheck/build, Alembic head check and script syntax checks.
• cd backend && python -m alembic heads passed: single head 202606120900.
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• Tower deploy via scripts/deploy_tower.ps1 passed after commit 1cbb356.
• Tower live migration smoke passed with PostGIS 3.4 and Alembic head 202606120900.
• Tower browser runtime verification passed on http://192.168.10.150:1202.
• Additional HTTP smoke passed for frontend HTML and GET /api/v1/projects through the frontend proxy.

Notes:

• No API contracts, backend behavior, migrations, product features, provider fetching, AI behavior or UI redesign changed.
• Next maintainability pass should split change detection, QA/QC results and map workspace controls into focused presentational components.

Sprint 28 dataset workflow hook hardening (2026-06-17)

Changed:

• Moved dataset selection, upload form state, dataset detail loading, dataset jobs and raster/vector operation orchestration from frontend/src/App.tsx into frontend/src/hooks/useDatasetWorkflow.ts.
• Kept project dataset listing in App.tsx so project/area loading remains the shared workbench boundary.
• Added regression tests to verify App uses the dataset workflow hook and still wires dataset, raster and vector callbacks.
• Updated frontend README, TODO and changelog docs.

Validation:

• python -m pytest backend/tests/test_sprint28_dataset_workflow_hook.py backend/tests/test_sprint27_frontend_workflow_hooks.py backend/tests/test_sprint26_frontend_workflow_hooks.py passed: 11 tests.
• python -m compileall backend/app passed.
• cd backend && python -m pytest passed: 167 tests.
• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• bash scripts/run_readiness_check.sh passed: 167 backend tests, frontend typecheck/build, Alembic head check and script syntax checks.
• cd backend && python -m alembic heads passed: single head 202606120900.
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• Initial Tower rebuild hit Docker btrfs storage exhaustion; safely reclaimed build cache only with docker builder prune -af without pruning volumes.
• Tower Docker storage recovered from /var/lib/docker 94% used to 58% used after cleanup and rebuild.
• Tower deploy via scripts/deploy_tower.ps1 passed after commit 361776c.
• Tower live migration smoke passed with PostGIS 3.4 and Alembic head 202606120900.
• Tower browser runtime verification passed on http://192.168.10.150:1202.
• Additional HTTP smoke passed for frontend HTML and GET /api/v1/projects through the frontend proxy.

Notes:

• No API contracts, backend behavior, migrations, product features, provider fetching, AI behavior or UI redesign changed.
• Next maintainability pass should split dataset detail, raster controls and vector controls into presentational components fed by the hook state.

Sprint 27 export and QA workflow hook hardening (2026-06-17)

Changed:

• Moved Export Center orchestration state and API calls from frontend/src/App.tsx into frontend/src/hooks/useExportWorkflow.ts.
• Moved QA/QC comparison state and persisted quality-check listing from frontend/src/App.tsx into frontend/src/hooks/useQualityWorkflow.ts.
• Added regression tests to verify App uses export/quality hooks and still wires QA/QC results plus ExportCenter callbacks.
• Updated frontend README, TODO and changelog docs.

Validation:

• python -m pytest backend/tests/test_sprint27_frontend_workflow_hooks.py backend/tests/test_sprint26_frontend_workflow_hooks.py passed: 8 tests.
• python -m compileall backend/app passed.
• cd backend && python -m pytest passed: 164 tests.
• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• bash scripts/run_readiness_check.sh passed: 164 backend tests, frontend typecheck/build, Alembic head check and script syntax checks.
• cd backend && python -m alembic heads passed: single head 202606120900.
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• Tower deploy via scripts/deploy_tower.ps1 passed after commit fd0000f.
• Tower live migration smoke passed with PostGIS 3.4 and Alembic head 202606120900.
• Tower browser runtime verification passed on http://192.168.10.150:1202.
• Additional HTTP smoke passed for frontend HTML and GET /api/v1/projects through the frontend proxy.

Notes:

• No API contracts, backend behavior, migrations, product features, provider fetching, AI behavior or UI redesign changed.
• Local Windows Docker CLI was unavailable (docker command not found); Tower deployment remains handled through scripts/deploy_tower.ps1.
• Next maintainability pass should extract dataset/raster/vector operation workflows from App.tsx.

Sprint 26 frontend workflow hook hardening (2026-06-17)

Changed:

• Moved Detection Lab orchestration state and API calls from frontend/src/App.tsx into frontend/src/hooks/useDetectionWorkflow.ts.
• Moved Segmentation Lab orchestration state and API calls from frontend/src/App.tsx into frontend/src/hooks/useSegmentationWorkflow.ts.
• Added shared frontend formatError helper under frontend/src/lib/formatError.ts.
• Added regression tests to verify App uses workflow hooks and still wires DetectionLab/SegmentationLab callbacks.
• Updated frontend README, TODO and changelog docs.

Validation:

• python -m pytest backend/tests/test_sprint26_frontend_workflow_hooks.py backend/tests/test_sprint22_workbench_status_strip.py passed: 6 tests.
• cd frontend && npm run typecheck passed.
• cd frontend && npm run build passed.
• bash scripts/run_readiness_check.sh passed: 160 backend tests, frontend typecheck/build, Alembic head check and script syntax checks.
• python -m compileall backend/app passed.
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• Tower deploy/live migration/browser runtime passed after commit 6c32f29.
• In-app browser check passed on http://192.168.10.150:1202: workbench, status strip, Detection Lab, Segmentation Lab and Export Center visible with no console error logs.

Notes:

• No API contracts, backend behavior, migrations, product features, provider fetching, AI behavior or UI redesign changed.

Sprint 25 YOLO compatibility smoke hardening (2026-06-17)

Changed:

• Added explicit --check-model-load support to scripts/yolo_preflight.py, backend/scripts/yolo_preflight.py and YoloPreflightService.
• The model-load smoke requires real optional AI dependencies, loads only an existing local model file, runs no inference and does not download weights.
• The CLI rejects --check-model-load with --assume-dependencies to avoid false-positive AI readiness.
• Added regression tests for mocked successful load, load failure reporting and CLI guard behavior.
• Added Python compile validation for both YOLO preflight entrypoints to the readiness gate.
• Updated AI pipeline, scripts, backend, TODO and changelog docs, including Docker runtime usage.

Validation:

• python -m py_compile scripts/yolo_preflight.py passed.
• python -m pytest backend/tests/test_sprint13_yolo_preflight.py backend/tests/test_readiness_gate.py passed: 14 tests.
• bash scripts/run_readiness_check.sh passed: 156 backend tests, frontend typecheck/build, Alembic head check and script syntax checks.
• python -m compileall backend/app passed.
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh passed.
• Tower deploy/live migration/browser runtime passed after commit 6c32f29.
• In-app browser check passed on http://192.168.10.150:1202: workbench, status strip, Detection Lab, Segmentation Lab and Export Center visible with no console error logs.
• Tower deploy/live migration/browser runtime passed after commit 382dcad.
• Backend-container YOLO preflight sanity passed: default status not_configured, will_download_models=false, will_run_inference=false.
• Backend-container CLI guard passed: --check-model-load with --assume-dependencies is rejected.

Notes:

• No base dependencies, API contracts, migrations, product features, provider fetching or detection persistence behavior changed.

Sprint 24 demo/export artifact cleanup tooling (2026-06-17)

Changed:

• Added scripts/cleanup_demo_artifacts.py for dry-run-first cleanup of old offline demo export artifacts.
• Added backend/scripts/cleanup_demo_artifacts.py so the same cleanup can run inside the backend Docker container.
• Cleanup is constrained to an exact demo project name by default, keeps the newest exports per project and refuses file deletion outside STORAGE_ROOT.
• Added regression tests for cleanup selection, path safety, dry-run candidate reporting and readiness gate coverage.
• Added Python compile validation for the cleanup script to scripts/run_readiness_check.sh.
• Documented cleanup usage in scripts/README.md, docs/STORAGE_ARCHITECTURE.md, backend/README.md, docs/TODO.md and CHANGELOG.md.

Validation:

• python -m py_compile scripts/cleanup_demo_artifacts.py passed.
• python -m pytest backend/tests/test_sprint24_cleanup_demo_artifacts.py backend/tests/test_readiness_gate.py passed: 10 tests.
• bash scripts/run_readiness_check.sh passed twice after adding the backend container entrypoint: 152 backend tests, frontend typecheck/build, Alembic head check and script syntax checks.
• python -m compileall backend/app passed.
• cd backend && python -m alembic upgrade head --sql passed.
• bash -n scripts/live_migration_smoke.sh and bash -n scripts/verify_demo_export_workflow.sh passed.
• Local docker compose config could not run because the Windows Docker CLI is not installed in this Codex environment.
• Tower deploy/live migration/browser runtime passed after final commit 2d4e6bd.
• Live backend-container cleanup dry-run exposed and then fixed confusing dry-run labeling so candidates are reported as candidate_files, not deleted_files; final dry-run reported 2 candidate files, 0 deleted files and 0 deleted export rows.

Notes:

• No API contracts, migrations, product features, provider fetching, AI inference or source dataset cleanup behavior changed.

Sprint 23 V1 report handoff summary (2026-06-17)

Changed:

• Added V1 readiness summary data to project metadata exports.
• Added V1 Readiness Summary and Known Limitations sections to lightweight HTML project report exports.
• Included persisted AOI, dataset readiness, QA/QC and export-history counts in the handoff summary.
• Updated export tests, API contract docs, frontend README, TODO and changelog.

Tested:

• Passed: backend compile, export tests through full readiness, full backend pytest, frontend typecheck/build, Alembic heads, Alembic SQL upgrade, live smoke syntax check, Tower deploy, live migration smoke and live project report smoke.

Known limitations:

• Report export remains a lightweight HTML artifact, not a PDF designer or custom report builder.

Next recommended pass:

• Add optional cleanup tooling for stale demo/export artifacts if repeated smoke runs keep accumulating local artifacts.

Sprint 22 V1 workbench status strip (2026-06-17)

Changed:

• Added frontend/src/components/WorkbenchStatusStrip.tsx to summarize existing V1 state for project, AOI, datasets, active map layer, QA/QC and exports.
• Wired the status strip into frontend/src/App.tsx using existing orchestration state only.
• Added compact status-strip styling and regression tests for the frontend wiring contract.n- Hardened frontend/src/components/GeoMap.tsx so MapLibre source/layer updates wait for style readiness before adding sources.n- Hardened demo project lookup so duplicate historical demo projects prefer complete fixture state before repairing incomplete state.
• Updated frontend README, TODO and changelog.

Tested:

• Passed: backend compile, focused pytest, full backend pytest with DeprecationWarning as error, frontend typecheck/build, Alembic heads, Alembic SQL upgrade, readiness via Git Bash and live smoke syntax check via Git Bash.

Known limitations:

• The strip is a read-only operator summary; it intentionally does not add new backend status APIs or product workflows.

Next recommended pass:

• Add a compact project handoff summary in exports/report output if the browser-facing V1 workflow remains green.

Sprint 21 V1 demo workflow smoke hardening (2026-06-17)

Changed:

• Hardened scripts/verify_demo_export_workflow.sh so the explicit offline demo smoke validates area GeoJSON, fixture datasets, vector FeatureCollection content, vector feature summaries, persisted QA/QC metrics and export downloads through the frontend proxy.
• Updated the frontend demo workflow action to open the candidate vector fixture dataset after seeding/loading the demo, so the Map Workbench is populated without a manual dataset click.
• Added regression tests for the strengthened smoke script and frontend demo loading contract.
• Updated scripts/frontend documentation, changelog and TODO status.

Tested:

• Passed: backend compile, backend pytest with DeprecationWarning as error, readiness, frontend typecheck/build, Alembic heads, Alembic SQL upgrade, Tower deploy, live migration smoke, browser-runtime smoke and expanded demo/export workflow smoke.

Known limitations:

• The demo smoke intentionally seeds fixture demo data when run; it should be used as an explicit verification command, not as an implicit healthcheck.

Next recommended pass:

• Add a compact V1 dashboard/status strip for project, AOI, datasets, QA and exports so operators can see readiness at a glance after opening a project.

Sprint 20 V1 selected area map overlay (2026-06-17)

Changed:

• Added GeoJSON geometry serialization for project areas so persisted AOIs can be displayed by the map workbench.
• Added a dedicated MapLibre area overlay layer with separate visibility and opacity controls.
• Added area list actions and map workspace controls to select the active AOI.
• Updated API/frontend docs, changelog and TODO status for selected area display.

Tested:

• Passed: backend compile, backend pytest with DeprecationWarning as error, readiness, frontend typecheck/build, Alembic heads, Alembic SQL upgrade, Tower deploy, live migration smoke, browser-runtime smoke and browser UI audit.

Known limitations:

• Area geometry is displayed as a simple filled/outlined GeoJSON overlay; no drawing/editing workflow is introduced in this pass.

Next recommended pass:

• Add a small V1 workflow polish pass for richer dataset/area empty states and a fixture-driven end-to-end browser smoke once the new build is deployed.

Sprint 19 V1 map workbench controls (2026-06-17)

Changed:

• Added active MapLibre layer visibility and opacity controls.
• Added click-to-inspect feature property display for the active GeoJSON workbench layer.
• Added active layer label and feature count to the Map workspace panel.
• Updated the app header from the stale Sprint 9 label to the GeoIntel Kempen V1 Workbench identity.
• Added regression tests for the frontend map control and feature inspection wiring.

Limitations:

• The current workbench still shows one active GeoJSON overlay at a time; multi-layer stack ordering remains a later UI enhancement.
• Raster preview display still remains metadata/path-oriented unless the backend exposes a browser-safe raster image/tile URL.
• No API contracts, migrations, backend behavior, provider fetching, AI inference or new dependencies were introduced.

Validation planned:

• python -m compileall backend/app
• cd backend && python -m pytest -W error::DeprecationWarning
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh via Git Bash on Windows
• Tower redeploy through scripts/deploy_tower.ps1

Sprint 18 vector change detection foundation (2026-06-16)

Changed:

• Added POST /api/v1/analysis/change-detection for comparing two vector datasets in the same project through the existing synchronous job envelope.
• Added ChangeDetectionService with persisted vector_features as the primary source of comparable geometries and explicit stored-GeoJSON fallback warnings for older datasets.
• Added frontend Change Detection controls, summary counts and MapLibre overlay styling for added, removed and unchanged feature properties.
• Added nginx no-cache headers for frontend HTML/assets after browser verification exposed stale cached modules on the LAN deployment.
• Added backend tests for persisted-vector comparison and canonical API envelope behavior.

Limitations:

• The foundation classifies added, removed and unchanged only. It does not emit fake changed objects without durable object ids/versioning.
• No migrations, live GRB/OSM/Sentinel fetching, AI inference, new dependencies, LiDAR, Copilot, Training Studio or separate Reports module were introduced.

Validation:

• python -m compileall backend/app
• cd backend && python -m pytest -W error::DeprecationWarning
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh via Git Bash on Windows
• cd backend && python -m alembic heads && python -m alembic upgrade head --sql

Codex Execution Log

This file must be updated by Codex after each implementation pass.

Format

## Pass X — Title
Date:

### Completed
- ...

### Files changed
- ...

### Tests run
- ...

### Known limitations
- ...

### Next recommended pass
- ...

Initial status

Specification phase completed through M0. No code foundation has been implemented yet.

M2 Engineering Package

• Add ADR decision records.
• Add RFC placeholders for future modules.
• Add API/database/event contracts.
• Add model registry and class catalogs.
• Add queue architecture.
• Add acceptance matrix and test catalog.
• Add Codex M2 build prompts.
• Start Codex Pass 01 backend foundation.

M8 preparation

Added the tomorrow execution layer for Codex autonomy: Day 1 master prompt, pass prompts, failure recovery, quality gates, operator checklist and smoke script scaffold.

Sprint 1 readiness hardening (2026-06-11)

Date: 2026-06-11

Completed

• Hardened backend dependency declarations for Sprint 1 runtime modules in backend/pyproject.toml.
• Normalized helper scripts for backend/frontend install/test/dev/readiness across python/python3 interpreter availability.
• Fixed PostGIS/geospatial stack compatibility issues (geojson_service import path, package config).
• Updated backend/frontend/docs/readme commands for import smoke, setup, and readiness verification.
• Fixed frontend shell script line-ending parsing failures and added npm availability checks.
• Added missing frontend type path fixes and TypeScript typing corrections.
• Added/verified minimal Sprint 1 tests for health and GeoJSON parsing/rejection behaviors.

Files changed

• backend/pyproject.toml
• backend/app/core/config.py
• backend/app/services/geojson_service.py
• backend/tests/test_health.py
• backend/tests/test_geojson_dataset_service.py
• backend/README.md
• frontend/package.json
• frontend/tsconfig.json
• frontend/src/components/GeoMap.tsx
• frontend/src/services/api/areas.ts
• frontend/src/services/api/projects.ts
• frontend/src/services/api/datasets.ts
• frontend/src/App.tsx
• frontend/README.md
• scripts/run_readiness_check.sh
• scripts/backend_install.sh
• scripts/backend_test.sh
• scripts/backend_dev.sh
• scripts/codex_pass_end_check.sh
• scripts/smoke_backend_import.sh
• scripts/frontend_install.sh
• scripts/frontend_typecheck.sh
• scripts/frontend_build.sh
• scripts/frontend_dev.sh
• README.md
• .env.example
• CHANGELOG.md
• Makefile

Tests run

• python -m compileall backend/app (pass)
• cd backend; python -m pytest (pass, 5 tests)
• cd backend; python -c "from app.main import app; print(app.title)" (pass)
• bash scripts/run_readiness_check.sh (pass)
• bash scripts/smoke_backend_import.sh (pass)
• bash scripts/frontend_install.sh (pass)
• bash scripts/frontend_typecheck.sh (pass)
• bash scripts/frontend_build.sh (pass)
• make-based targets not runnable in this environment (make command missing)
• docker compose config not runnable in this environment (docker command missing)
• python3 commands fail in this environment because python3 maps to Microsoft Store stub; use python instead.

Known limitations

• docker and make are not installed in the current local shell environment.
• python3 is not a usable interpreter in this environment; python must be used for all backend runtime/tests.

Next recommended pass

• Run docker compose validation and DB-backed migration checks in an environment with Docker + PostGIS service available.

Pass 12 — Sprint 2 Foundation

Date: 2026-06-11

Completed

• Added vector/raster dataset typing and lifecycle states (uploaded, validating, ready, failed) in dataset service.
• Added vector metadata extraction details (feature counts, geometry types, bounds, area, CRS assumptions).
• Added raster metadata service with dependency-aware unavailable behavior and explicit RASTER_PROCESSING_UNAVAILABLE handling.
• Added dataset vector inspect/summary and raster metadata endpoints for project-scoped datasets.
• Persisted deterministic storage metadata for uploads (original/stored filename, MIME, size, checksum).
• Extended frontend dataset manager details panel with type/status/file metadata/feature counts and raster summary readiness.
• Added minimal Sprint 2 tests for vector metadata, legacy geojson compatibility, storage metadata persistence, and raster dependency fallback.

Files changed

• backend/app/services/dataset_service.py
• backend/app/services/raster_service.py
• backend/app/services/geojson_service.py
• backend/app/schemas/dataset.py
• backend/app/api/routes/datasets.py
• backend/app/services/storage_service.py
• backend/tests/test_geojson_dataset_service.py
• backend/tests/test_raster_service.py
• backend/tests/test_storage_service.py
• backend/README.md
• frontend/src/App.tsx
• frontend/src/services/api/datasets.ts
• frontend/src/types.ts
• docs/API_CONTRACTS.md
• docs/CODEX_EXECUTION_LOG.md
• .env.example
• backend/README.md
• frontend/README.md
• CHANGELOG.md
• README.md

Tests run

• Sprint 2 tests to be executed via readiness commands (see below).

Known limitations

• rasterio is not required by default; raster metadata extraction is unavailable until installed in the environment.
• Docker and PostGIS validation still depends on local availability of Docker/DB runtime.

Next recommended pass

• Keep Sprint 1/2 architecture; implement raster clipping/tiling APIs and status jobs before Detection/Segmentation modules.

Pass 13 — Sprint 2 validation and legacy compatibility hardening

Date: 2026-06-11

Completed

• Verified Sprint 2 readiness commands in current environment (where tools are available).
• Revalidated backend tests and frontend typecheck/build after a Sprint 2 compatibility patch.
• Added frontend vector-detail compatibility for legacy geojson dataset rows in Sprint 1 records.
• Confirmed backend import smoke and dataset/raster/vector service behavior remain intact.

Files changed

• frontend/src/App.tsx
• docs/CODEX_EXECUTION_LOG.md

Tests run

• python -m compileall backend/app (pass)
• cd backend && python -m pytest (pass, 11 tests)
• bash scripts/run_readiness_check.sh (pass)
• bash scripts/smoke_backend_import.sh (pass)
• cd frontend && npm run typecheck (pass)
• cd frontend && npm run build (pass)
• cd backend; python3 -m compileall backend/app (fails: python3 shim unavailable in this shell)
• cd backend; python3 -c "from app.main import app; print(app.title)" (fails: python3 shim unavailable in this shell)
• docker compose config (fails: docker command unavailable in this shell)

Known limitations

• python3 is not available in the current environment; use python commands for verification.
• docker is not available in the current environment.

Next recommended pass

• Run the same sprint verification commands in an environment with python3 and docker installed.

Pass 14 — Sprint 3 operations + job foundation

Date: 2026-06-11

Completed

• Added lightweight job model/migration and service layer with statuses queued, running, success, failed.
• Added job API endpoints for create/list/read/status under project scope.
• Added vector operation service foundation:
  • inspect
  • bbox
  • stats
  • clip by area
  • buffer
  • intersect
• Added raster operation foundation:
  • inspect
  • metadata
  • preview readiness
  • clip placeholder (dependency-aware)
  • tile placeholder (dependency-aware)
• Added job-based execution wrappers for vector/raster operation endpoints.
• Updated dataset detail UI with available operations, job list/status, and derived output navigation.
• Added/updated Sprint 3 API contract and backend/frontend documentation updates.

Files changed

• backend/app/schemas/operations.py
• backend/app/schemas/__init__.py
• backend/app/services/vector_operations_service.py
• backend/app/services/raster_operations_service.py
• backend/app/services/job_service.py
• backend/app/api/routes/jobs.py
• backend/app/models/entities.py
• backend/app/models/__init__.py
• backend/alembic/versions/20260611212435_add_jobs_table.py
• backend/tests/test_vector_operations_service.py
• backend/tests/test_raster_operations_service.py
• backend/tests/test_storage_service.py
• frontend/src/App.tsx
• frontend/src/services/api/datasets.ts
• frontend/src/services/api/jobs.ts
• frontend/src/types.ts
• backend/README.md
• frontend/README.md
• docs/API_CONTRACTS.md
• docs/CODEX_EXECUTION_LOG.md
• CHANGELOG.md

Tests run

• python -m compileall backend/app (pass)
• cd backend && python -m pytest (pass)
• bash scripts/run_readiness_check.sh (pass)
• bash scripts/smoke_backend_import.sh (pass)
• cd frontend && npm run typecheck (pass)
• cd frontend && npm run build (pass)

Known limitations

• Raster processing beyond readiness/metadata is intentionally dependency-aware placeholder in Sprint 3.
• docker remains unavailable in this environment.
• python3 still resolves to Windows Store stub; use python for verification.

Next recommended pass

• Install rasterio/GDAL for real raster clipping/tile implementations.
• Add queue-backed worker (Redis/RQ) behind existing synchronous job facade.
• Add deeper operation acceptance tests for output dataset geometry correctness and persisted metadata.

Pass 15 — Sprint 4 raster foundation

Date: 2026-06-11

Completed

• Finalized dependency-aware raster processing behavior for clip/tile/preview and improved metadata enrichment.
• Ensured raster metadata and preview responses include deterministic file metadata (size_bytes, checksum_sha256, path) where available.
• Added raster tile manifest generation with deterministic storage paths and returned manifest payload.
• Updated API contracts and execution documentation from placeholder wording to actual raster-op foundations.
• Added targeted Sprint 4 tests for missing-area clip behavior and tile manifest payload shape.

Files changed

• backend/app/services/raster_operations_service.py
• backend/app/api/routes/health.py
• backend/tests/test_raster_operations_service.py
• docs/API_CONTRACTS.md
• docs/CODEX_EXECUTION_LOG.md
• backend/README.md
• frontend/README.md
• README.md
• CHANGELOG.md

Tests run

• python -m compileall backend/app
• cd backend && python -m pytest
• bash scripts/run_readiness_check.sh
• bash scripts/smoke_backend_import.sh
• cd frontend && npm run typecheck
• cd frontend && npm run build

Known limitations

• Raster dependency packages remain optional and will report RASTER_PROCESSING_UNAVAILABLE if absent.
• Raster clip/tile remain synchronous under the current in-process job wrapper.

Next recommended pass

• Run full raster end-to-end tests with real GeoTIFF fixtures and validate output dataset metadata persistence.

Pass 16 � Sprint 4 raster operations foundation hardening

Date: 2026-06-11

Completed

• Realized the Sprint 4 raster foundation readiness in implementation and docs alignment:
  • metadata extraction now returns real raster profile fields when rasterio is available
  • preview generation writes deterministic PNG artifacts and reuses cached previews
  • clip and tile operations persist deterministic outputs and manifest structure
  • all raster processing paths now use explicit dependency-aware errors when rasterio/numpy/pillow are missing
• Fixed remaining frontend render/type issues introduced during raster path handling.
• Strengthened raster tests for tile manifest minimum size and dependency-aware behavior.
• Updated sprint milestone docs to reflect Sprint 4 status:
  • backend/README.md
  • frontend/README.md

Files changed

• backend/app/services/raster_operations_service.py
• backend/tests/test_raster_operations_service.py
• backend/app/api/routes/datasets.py
• backend/app/schemas/operations.py
• frontend/src/App.tsx
• backend/README.md
• frontend/README.md
• docs/CODEX_EXECUTION_LOG.md

Tests run

• python -m compileall backend/app (pass)
• cd backend && python -m pytest (pass, 26 tests)
• bash scripts/run_readiness_check.sh (pass)
• bash scripts/smoke_backend_import.sh (pass)
• cd frontend && npm run typecheck (pass)
• cd frontend && npm run build (pass)

Known limitations

• docker command is unavailable in this environment, so docker compose config could not be executed.
• python3 command is unavailable as an alias in this environment; use python instead.
• Full raster dependency stack may be unavailable in some dev environments; those cases intentionally return structured RASTER_PROCESSING_UNAVAILABLE responses.

Next recommended pass

• Sprint 5: add end-to-end RasterOps coverage for CRS-preserving re-projection, export-quality metadata policy, and tile set artifact cleanup lifecycle.

Pass 17 — Sprint 5 raster analytics hardening

Date: 2026-06-11

Completed

• Implemented Sprint 5 raster analytics foundation:
  • band statistics endpoint and service with dependency-aware unavailable mode.
  • reproject operation with CRS validation and reprojection metadata persistence.
  • clip/tile hardening for missing/invalid geometry and empty clip output handling.
  • enriched tile manifest fields (tile_set_id, tile_size, overlap, source_dataset_id, source_raster_id, bounds, count, tile_paths, ai_inference, tile_server, created_at, parameters).
• Updated raster stats/job error tests:
  • dependency-aware stats failure coverage.
  • invalid CRS request validation for reproject.
  • persisted derived dataset assertions for reproject and clip.
  • failure persistence test for failed raster jobs (_run_job_sync).
• Strengthened raster frontend detail panel rendering:
  • job result JSON is visible for raster/vector operations.
  • clearer raster metadata/status visibility retained for CRS/bounds/resolution display.
• Updated docs:
  • backend/README Sprint 5 section.
  • frontend/README Sprint 5 section.
  • CHANGELOG entry for Sprint 5.

Files changed

• backend/app/services/raster_operations_service.py (final reproject and manifest hardening alignment)
• backend/tests/test_raster_operations_service.py
• frontend/src/App.tsx
• backend/README.md
• frontend/README.md
• docs/API_CONTRACTS.md
• docs/RASTER_OPERATIONS_SPEC.md
• docs/CODEX_EXECUTION_LOG.md
• CHANGELOG.md

Tests run

• python -m compileall backend/app (pass)
• cd backend && python -m pytest (pass)
• bash scripts/run_readiness_check.sh (pass)
• bash scripts/smoke_backend_import.sh (pass)
• cd frontend && npm run typecheck (pass)
• cd frontend && npm run build (pass)

Known limitations

• Raster statistics and reproject operations still depend on environment availability of rasterio/numpy.
• Raster dependency checks and operation errors remain dependency-aware when libraries are unavailable.

Next recommended pass

• Sprint 6: add raster index/mask workflows (NDVI/NDWI/NDBI), tile serving or export packaging, and AI-ready dataset linking.

Pass 18 - Sprint 6 spectral indices

Completed

• Added local raster index operations (ndvi, ndwi, ndbi) under the existing raster operation architecture.
• Added typed band payload validation and explicit INVALID_PARAMETERS handling for missing/invalid band indexes.
• Implemented dependency-aware failure behavior for index execution (RASTER_PROCESSING_UNAVAILABLE) when rasterio or numpy are unavailable.
• Implemented local index output generation with float32 raster derivation and persisted provenance metadata on derived datasets.
• Extended dataset detail UI with spectral index controls (NDVI/NDWI/NDBI), run actions, and output dataset navigation from jobs.
• Updated API contracts, raster operation spec, and project documentation for Sprint 6 behavior.

Files changed

• backend/app/schemas/operations.py
• backend/app/services/raster_operations_service.py
• backend/app/api/routes/datasets.py
• backend/tests/test_raster_operations_service.py
• frontend/src/services/api/datasets.ts
• frontend/src/App.tsx
• frontend/src/types.ts
• docs/API_CONTRACTS.md
• docs/RASTER_OPERATIONS_SPEC.md
• backend/README.md
• frontend/README.md
• CHANGELOG.md
• docs/CODEX_EXECUTION_LOG.md

Tests run

• python -m compileall backend/app
• cd backend && python -m pytest
• bash scripts/run_readiness_check.sh
• bash scripts/smoke_backend_import.sh
• cd frontend && npm run typecheck
• cd frontend && npm run build

Known limitations

• Raster dependency checks remain optional; missing raster packages return explicit RASTER_PROCESSING_UNAVAILABLE results.
• Job execution remains synchronous under current Sprint 3 job facade.
• docker and python3 availability still depend on developer environment.

Next recommended pass

• Keep operation architecture stable, then add threshold/mask workflows and output export packaging in a follow-up pass.

Pass 19 - RC-2 stabilization

Date: 2026-06-12

Completed

• Fixed backend compile/import blockers identified in RC-1:
  • corrected dataset upload parameter ordering.
  • corrected schema package re-exports for area schemas.
• Fixed frontend typecheck/build blockers in App.tsx.
• Added Alembic migration for dataset reference/provenance metadata columns required by current ORM models.
• Fixed QA comparison runtime crash and added focused QA service coverage.
• Corrected envelope response-model mismatches for vector inspect and raster stats endpoints.
• Strengthened scripts/run_readiness_check.sh so readiness runs backend compile, backend tests, frontend typecheck and frontend build.
• Improved readiness Python interpreter selection so it chooses an interpreter capable of running pytest.

Files changed

• backend/app/services/dataset_service.py
• backend/app/schemas/__init__.py
• backend/app/api/routes/datasets.py
• backend/app/services/qa_service.py
• backend/alembic/versions/202606120001_add_dataset_reference_metadata.py
• backend/tests/test_qa_service.py
• frontend/src/App.tsx
• scripts/run_readiness_check.sh
• docs/CODEX_EXECUTION_LOG.md

Tests run

• python -m compileall backend\\app (pass)
• cd backend && python -m pytest (pass, 40 tests)
• cd frontend && npm run typecheck (pass)
• cd frontend && npm run build (pass; bundle-size warning only)
• bash scripts/run_readiness_check.sh (pass)
• cd backend && python -m alembic heads (pass, single head 202606120001)
• cd backend && python -m alembic upgrade head --sql (pass, generated SQL includes required dataset metadata columns)
• docker compose config not runnable in this environment because docker is not installed.

Known limitations

• Fresh online database migration was validated only as generated Alembic SQL in this environment; run cd backend && python -m alembic upgrade head against a live PostgreSQL/PostGIS database on a machine with Docker or Postgres available.
• Existing non-RC architecture limitations from RC-1 remain intentionally unfixed: synchronous job facade, file-first vector outputs, monolithic frontend component, and incomplete future AI/storage architecture.

Next recommended pass

• Do not start Sprint 7 until RC-2 verification is repeated against a live Docker/PostGIS environment.

Pass 20 - Sprint 7A persistence and QA foundation

Date: 2026-06-12

Completed

• Added first-class vector_features ORM model and Alembic migration with dataset and GiST geometry indexes.
• Persisted uploaded vector GeoJSON features into PostGIS-backed vector_features while keeping original file storage intact.
• Added first-class quality_checks and metrics ORM models and Alembic migration indexes.
• Added QualityService for persisted QA/QC domain records and metric rows.
• Updated QA candidate-vs-reference route so successful QA jobs also persist a QualityCheck and metrics, and return quality_check_id in result_json.
• Hardened provider capability contracts for GRB and OSM as not_configured stubs with supported layers, geometry types and query modes.
• Added Sprint 7A tests for vector feature persistence, quality check persistence, metrics persistence, dataset role validation, provider contracts, migration integrity and QA route persistence.
• Updated database/API documentation for Vector Features Architecture, Quality Check Architecture, Metrics Architecture and Provider Architecture.

Files changed

• backend/app/models/entities.py
• backend/app/models/__init__.py
• backend/app/services/dataset_service.py
• backend/app/services/vector_feature_service.py
• backend/app/services/quality_service.py
• backend/app/api/routes/qa.py
• backend/app/providers/base.py
• backend/app/providers/grb.py
• backend/app/providers/osm.py
• backend/app/schemas/health.py
• backend/alembic/versions/202606120700_sprint7a_persistence_foundation.py
• backend/tests/test_sprint7a_persistence_foundation.py
• docs/DATABASE_IMPLEMENTATION_PLAN.md
• docs/API_CONTRACTS.md
• docs/CODEX_EXECUTION_LOG.md
• CHANGELOG.md

Tests run

• python -m compileall backend/app (pass)
• python -m pytest backend -q (pass, 47 tests)

Known limitations

• Sprint 7A intentionally does not implement GRB downloads, OSM downloads, Detection Lab, Segmentation Lab, LiDAR, AI Copilot, Training Studio or Reports.
• Job execution remains the existing synchronous facade.
• Live database migration still needs validation against a running PostgreSQL/PostGIS service in an environment with Docker or Postgres available.

Next recommended pass

• Complete full release validation commands, including Alembic heads/SQL generation, readiness script, frontend typecheck/build and Docker config if Docker is available.

Validation addendum

Date: 2026-06-12

Additional Sprint 7A validation completed after migration index cleanup:

• python -m compileall backend/app (pass)
• cd backend && python -m pytest (pass, 47 tests)
• bash scripts/run_readiness_check.sh (pass)
• cd frontend && npm run typecheck (pass)
• cd frontend && npm run build (pass; Vite chunk-size warning only)
• cd backend && python -m alembic heads (pass, single head 202606120700)
• cd backend && python -m alembic upgrade head --sql (pass; generated SQL includes vector_features, quality_checks, metrics and the named GiST index ix_vector_features_geometry)
• docker compose config could not run because Docker is not installed in this shell.

Pass 21 - Sprint 7B provider integration skeleton (2026-06-12)

• Implemented central provider registry for grb, osm, manual and fixture.
• Added provider capability, layer, status and future import-contract endpoints using the existing response envelope style.
• Preserved GRB and OSM as explicit not_configured providers; no live WFS, Overpass, download or fake provider data was introduced.
• Documented and tested provider-to-dataset mapping rules; future provider output must flow through DatasetService / VectorFeatureService rather than direct vector_features writes.
• Added frontend Provider Capabilities panel without live import buttons for GRB/OSM.
• Added opt-in scripts/live_migration_smoke.sh for real PostGIS migration smoke checks.
• Added Sprint 7B tests for provider registry, API envelopes, invalid provider handling, import contract and smoke script presence.

Pass 22 - Sprint 8 Detection Lab foundation (2026-06-12)

Completed

• Added first-class detections ORM model and Alembic migration with project, dataset, analysis run, class and GiST geometry indexes.
• Hardened analysis_runs with dataset, job, model, result and created-at fields while keeping jobs conceptually separate from analysis lifecycle.
• Added model registry capability service for yolo-placeholder (not_configured) and manual-fixture-detector (explicit fixture/demo only).
• Added DetectionService boundary for model listing, request validation, analysis run creation, job creation, unavailable model responses and explicit fixture persistence.
• Added Detection Lab API endpoints under /api/v1/detection using the existing response envelope style.
• Added minimal frontend Detection Lab panel for model capability status, raster dataset selection, confidence threshold and run result/error display.
• Updated database, API, AI pipeline, backend/frontend README, TODO and changelog docs.

Known limitations

• Real YOLO/PyTorch inference is not enabled and no model downloads are performed.
• Fixture detector requires fixture_mode=true and explicit fixture detections; it is not production inference.
• Segmentation, LiDAR, AI Copilot, Training Studio and Reports remain out of scope.

Next recommended pass

• Verify Sprint 8 with full backend/frontend/readiness/Alembic gates, then perform a Sprint 8 verification audit before Sprint 8B real YOLO integration.

Pass 23 - Sprint 8B configured YOLO foundation (2026-06-12)

Completed

• Added optional backend ai dependency group for ultralytics and torch; normal backend startup remains import-safe without those packages.
• Added YOLO configuration settings:
  • YOLO_ENABLED
  • YOLO_MODEL_PATH
  • YOLO_MODEL_ID
  • YOLO_MODEL_DISPLAY_NAME
  • YOLO_MODEL_VERSION
  • YOLO_DEVICE
  • YOLO_IMAGE_SIZE
  • YOLO_MAX_TILES
  • YOLO_BATCH_SIZE
• Added yolo-configured model registry capability with honest not_configured, dependency_unavailable and configured states.
• Added YoloDetectionAdapter that imports Ultralytics only in the load path and refuses missing local model files before model construction.
• Added raster tile manifest validation and configured tile-limit enforcement for real YOLO runs.
• Added pixel bbox to EPSG:4326 polygon georeferencing from tile transform or bounds metadata.
• Routed configured YOLO outputs through existing DetectionService, Job, AnalysisRun and first-class Detection persistence.
• Added Detection Lab tile manifest path input for the configured YOLO model.
• Added mocked Sprint 8B tests for model registry status, dependency-unavailable behavior, tile manifest validation, georeferencing and persisted detections.
• Updated API, AI pipeline, backend/frontend README and changelog documentation.

Known limitations

• Sprint 8B does not add workers/queues; configured YOLO runs remain synchronous behind the existing job abstraction.
• Real model loading is validated at execution time. The registry reports configured when dependencies and local model path are present.
• No model weights are downloaded by GeoIntel.
• Detection visualization/map overlays are deferred.
• Segmentation, LiDAR, AI Copilot, Training Studio and Reports remain out of scope.

Next recommended pass

• Run full Sprint 8B validation and then perform a Sprint 8B verification audit before advancing to detection visualization/QA or segmentation planning.

Pass 24 - Sprint 8C detection visualization and QA integration (2026-06-12)

Completed

• Added detection result review endpoints for listing runs, listing detections by run/dataset, retrieving detection detail and returning persisted detections as GeoJSON FeatureCollections.
• Added lightweight detection filters for class name and minimum confidence.
• Added detection QA against persisted reference vector_features using the existing QualityService, quality_checks and metrics persistence path.
• Added frontend Detection Lab run selection, detection table, class/confidence filters and MapLibre detection GeoJSON overlay via the existing map component.
• Added frontend detection QA controls and metric summary display.
• Added Sprint 8C tests for GeoJSON output, list/filter behavior, detection detail, API envelope shape, QA persistence and no-match QA behavior.
• Added direct Sprint 8B tests for missing and invalid tile manifest files.
• Updated API, AI pipeline, backend/frontend README and changelog documentation.

Known limitations

• Detection QA requires reference datasets to have persisted vector_features; unsupported references return a clear error instead of fake metrics.
• Detection overlays reuse the existing single GeoJSON map layer styling; complex class-based map styling is deferred.
• Segmentation, LiDAR, AI Copilot, Training Studio and Reports remain out of scope.

Next recommended pass

• Run full Sprint 8C validation and perform a Sprint 8C verification audit before starting Sprint 9 Segmentation Lab.

Pass 25 - Sprint 9 Segmentation Lab foundation (2026-06-12)

Completed

• Added first-class segmentations ORM model and Alembic migration with project, dataset, job, analysis run, class and GiST geometry indexes.
• Added segmentation model registry capabilities for segmentation-placeholder, fixture-segmenter, yolo-seg-configured and sam-configured.
• Added SegmentationService boundary for raster validation, job/analysis-run lifecycle, unavailable model responses and explicit fixture-only persistence.
• Added segmentation adapter placeholder module with no SAM, YOLO-seg, torch or ultralytics imports.
• Added persisted segmentation GeoJSON output generated from PostGIS geometry and provenance properties.
• Added segmentation QA against persisted reference vector_features using existing quality_checks and metrics.
• Added minimal frontend Segmentation Lab panel for model states, raster selection, runs/results, GeoJSON map overlay and QA metric display.
• Updated API, AI pipeline, storage, database, backend/frontend README and changelog documentation.

Files changed

• backend/app/models/entities.py
• backend/app/models/__init__.py
• backend/alembic/versions/202606120900_sprint9_segmentation_foundation.py
• backend/app/schemas/segmentation.py
• backend/app/schemas/__init__.py
• backend/app/services/model_registry_service.py
• backend/app/services/segmentation_adapter.py
• backend/app/services/segmentation_service.py
• backend/app/api/routes/segmentation.py
• backend/app/main.py
• backend/tests/test_sprint9_segmentation_foundation.py
• frontend/src/types.ts
• frontend/src/services/api/segmentation.ts
• frontend/src/services/api/index.ts
• frontend/src/App.tsx
• docs/API_CONTRACTS.md
• docs/AI_PIPELINES.md
• docs/STORAGE_ARCHITECTURE.md
• docs/DATABASE_IMPLEMENTATION_PLAN.md
• backend/README.md
• frontend/README.md
• CHANGELOG.md
• docs/CODEX_EXECUTION_LOG.md

Tests run

• python -m pytest backend/tests/test_sprint9_segmentation_foundation.py -q (red first: missing Segmentation import, then pass)
• cd backend && python -m pytest (pass, 88 tests)
• cd frontend && npm run typecheck (pass)

Known limitations

• Sprint 9 intentionally does not implement real SAM, real YOLO-seg, model downloads, new AI dependencies or production-scale async inference.
• Fixture segmenter requires explicit fixture_mode=true and explicit fixture segmentations; it is not production inference.
• Metric area is only persisted when provided by the fixture/output payload; Sprint 9 does not compute authoritative area from masks.

Next recommended pass

• Run full Sprint 9 validation and then perform Sprint 9 Verification Audit before considering future real SAM/YOLO-seg integration.

Validation addendum

• python -m compileall backend/app (pass)
• cd backend && python -m pytest (pass, 88 tests)
• bash scripts/run_readiness_check.sh (pass)
• cd frontend && npm run typecheck (pass)
• cd frontend && npm run build (pass; existing Vite chunk-size warning only)
• cd backend && python -m alembic heads (pass, single head 202606120900)
• cd backend && python -m alembic upgrade head --sql (pass; generated SQL includes segmentations and GiST index)
• bash -n scripts/live_migration_smoke.sh (pass)
• docker compose config could not run because Docker is not installed in this shell.

Pass 26 - Sprint 10 release hardening and frontend modularization (2026-06-13)

Completed

• Extracted Provider Capabilities, Detection Lab and Segmentation Lab sections from frontend/src/App.tsx into focused frontend components.
• Preserved existing workbench state ownership, API client calls, map overlay behavior and UI copy.
• Hardened scripts/run_readiness_check.sh with Alembic head verification and live migration smoke script syntax validation.
• Updated frontend README and changelog documentation for Sprint 10 maintainability work.

Files changed

• frontend/src/App.tsx
• frontend/src/components/providers/ProviderPanel.tsx
• frontend/src/components/detection/DetectionLab.tsx
• frontend/src/components/segmentation/SegmentationLab.tsx
• scripts/run_readiness_check.sh
• frontend/README.md
• CHANGELOG.md
• docs/CODEX_EXECUTION_LOG.md

Known limitations

• Sprint 10 intentionally does not add new backend capabilities, migrations, product features, AI dependencies or live provider fetching.
• App.tsx still owns shared workbench state orchestration; further extraction can be considered in a later maintainability pass if needed.

Sprint 10 addendum - additional frontend extraction

• Extracted frontend/src/components/project/ProjectPanel.tsx and frontend/src/components/project/AreaPanel.tsx from frontend/src/App.tsx.
• Kept project and area form state owned by App.tsx; extracted components receive state and callbacks only.
• cd frontend && npm run typecheck passed after the additional extraction.

Pass 27 - Sprint 11 Live Docker/PostGIS Runtime Validation (2026-06-13)

Completed

• Hardened scripts/live_migration_smoke.sh so it runs SELECT 1, applies alembic upgrade head, then checks PostGIS_Version().
• Added migrated schema-object checks for core tables and geometry indexes after the live migration step.
• Added backend/tests/test_live_migration_smoke_script.py to lock the smoke-script ordering and schema-check contract.
• Documented the Docker/PostGIS validation command sequence, expected local DATABASE_URL and cleanup commands in backend/README.md.

Files changed

• scripts/live_migration_smoke.sh
• backend/tests/test_live_migration_smoke_script.py
• backend/README.md
• CHANGELOG.md
• docs/CODEX_EXECUTION_LOG.md

Runtime status

• Docker is not installed or not available in this shell, so docker compose config, docker compose up -d db and the live container-backed smoke could not be completed here.
• On a Docker-enabled machine, run:
  • docker compose config
  • docker compose up -d db
  • DATABASE_URL=postgresql+psycopg://geointel:geointel@localhost:5432/geointel bash scripts/live_migration_smoke.sh

Known limitations

• Sprint 11 did not add product behavior, API contracts, migrations, AI dependencies or provider fetching.
• Live runtime validation is partially blocked until Docker/PostGIS is available in the execution environment.

Pass 28 - Sprint 12 QA/QC golden dataset and benchmarking (2026-06-15)

Completed

• Added deterministic golden QA/QC fixtures for reference and predicted building polygons.
• Added fixtures/golden/expected_qa_metrics.json with the expected partial-match baseline.
• Added scripts/run_golden_qa_benchmark.py to run existing QaService logic and verify QualityService persistence output.
• Added backend tests for expected golden metrics, benchmark command output and persisted metric keys.
• Updated QA/QC specification, backend README and changelog documentation.

Files changed

• fixtures/golden/reference_buildings.geojson
• fixtures/golden/predicted_buildings.geojson
• fixtures/golden/expected_qa_metrics.json
• scripts/run_golden_qa_benchmark.py
• backend/tests/test_sprint12_golden_qa_benchmark.py
• docs/QA_QC_SPECIFICATION.md
• backend/README.md
• CHANGELOG.md
• docs/CODEX_EXECUTION_LOG.md

Expected benchmark metrics

• precision: 0.5
• recall: 0.5
• F1: 0.5
• mean IoU: 0.8339768339761133
• false positives: 1
• false negatives: 1

Known limitations

• The benchmark uses explicit fixture/demo data and an in-memory persistence session; it does not replace the pending Docker/PostGIS live smoke.
• Sprint 12 does not add product features, API contracts, migrations, live providers, AI model execution or new dependencies.

Pass 29 - Sprint 13 Real YOLO operational hardening (2026-06-15)

Completed

• Added YoloPreflightService for local configured-YOLO readiness checks without model loading, inference or downloads.
• Added scripts/yolo_preflight.py for CLI checks of enabled state, dependencies, local model file, tile manifest validity, tile limit and tile paths.
• Added Sprint 13 backend tests for disabled, dependency-unavailable and ready preflight states plus CLI JSON output.
• Updated AI pipeline, backend README and changelog documentation.

Files changed

• backend/app/services/yolo_preflight_service.py
• scripts/yolo_preflight.py
• backend/tests/test_sprint13_yolo_preflight.py
• docs/AI_PIPELINES.md
• backend/README.md
• CHANGELOG.md
• docs/CODEX_EXECUTION_LOG.md

Known limitations

• Preflight does not prove model compatibility or inference correctness; it intentionally avoids loading YOLO models.
• Optional AI dependencies are still not installed by default.
• Docker/PostGIS live validation remains pending until Docker is available.

Pass 30 - Release hardening audit pass (2026-06-15)

Completed

• Audited release-readiness signals after Sprint 13, including timestamp warnings, frontend bundle output, migration SQL rendering and readiness coverage.
• Replaced backend datetime.utcnow() calls with timezone-aware UTC timestamps in service paths.
• Verified the affected backend tests with DeprecationWarning promoted to errors.
• Split frontend production output into app, React vendor and MapLibre vendor chunks, with an explicit chunk warning threshold for the known MapLibre GIS runtime.
• Updated backend/frontend README, TODO and changelog documentation.

Files changed

• backend/app/services/dataset_service.py
• backend/app/services/geojson_service.py
• backend/app/services/job_service.py
• backend/app/services/qa_service.py
• backend/app/services/quality_service.py
• frontend/vite.config.ts
• backend/README.md
• frontend/README.md
• docs/TODO.md
• CHANGELOG.md
• docs/CODEX_EXECUTION_LOG.md

Known limitations

• This pass does not add product features, migrations, API contracts, AI dependencies, provider fetching or model execution.
• Docker/PostGIS live validation still requires a Docker-enabled machine.
• Larger frontend architectural decomposition remains a separate low-risk planning item; this pass only hardened build output.

Pass 31 - Extended release hardening sweep (2026-06-15)

Completed

• Promoted the backend readiness gate to run pytest with -W error::DeprecationWarning.
• Added backend tests that verify readiness and pass-end scripts keep the stricter release checks in place.
• Hardened scripts/codex_pass_end_check.sh so placeholder scans skip node_modules, dist and __pycache__ folders.
• Updated docs/TODO.md with a current implementation status layer while preserving older planning context.
• Re-ran pass-end checks and strict backend warning checks.

Files changed

• scripts/run_readiness_check.sh
• scripts/codex_pass_end_check.sh
• backend/tests/test_readiness_gate.py
• backend/README.md
• docs/TODO.md
• CHANGELOG.md
• docs/CODEX_EXECUTION_LOG.md

Known limitations

• This pass still does not add API contracts, migrations, product features, provider fetching, AI dependencies or model execution.
• Docker/PostGIS live validation remains blocked in this local environment because Docker is unavailable.

Pass 32 - Readiness contract gate hardening (2026-06-15)

Completed

• Added API contract smoke validation to scripts/run_readiness_check.sh.
• Added a regression test that requires the readiness gate to keep running scripts/smoke_contracts.py.
• Re-ran the full readiness gate after the change.

Files changed

• scripts/run_readiness_check.sh
• backend/tests/test_readiness_gate.py
• backend/README.md
• CHANGELOG.md
• docs/CODEX_EXECUTION_LOG.md

Known limitations

• Docker/PostGIS live validation remains blocked in this local environment because Docker is unavailable.

Pass 33 - Docker runtime build hardening (2026-06-15)

Completed

• Investigated Unraid/Tower Docker build failure from pasted server output.
• Fixed backend Docker build ordering so README.md and app/ exist before pip install ..
• Removed mandatory root .env references from Compose; default local runtime now uses checked-in environment values.
• Added PostGIS healthcheck and backend depends_on health condition.
• Made the backend container run python -m alembic upgrade head before starting Uvicorn.
• Added backend and frontend .dockerignore files to reduce Docker build context and exclude dependency/build/cache outputs.
• Added regression tests for Dockerfile package-source ordering, Compose env behavior, DB health/migration startup and Docker ignore coverage.

Files changed

• backend/Dockerfile
• docker-compose.yml
• backend/.dockerignore
• frontend/.dockerignore
• backend/tests/test_docker_runtime_config.py
• README.md
• backend/README.md
• docs/TODO.md
• CHANGELOG.md
• docs/CODEX_EXECUTION_LOG.md

Known limitations

• Docker commands still cannot be executed in this local Codex environment because Docker is unavailable here.
• The server should rerun docker compose build --no-cache && docker compose up -d to verify the real Docker runtime.

Pass 34 - Docker browser port 1202 (2026-06-15)

Completed

• Changed Docker Compose frontend host publishing from 5173:5173 to 1202:5173.
• Added backend Docker CORS defaults for http://localhost:1202 and http://127.0.0.1:1202.
• Updated .env.example and local/Docker documentation to point browser users to http://localhost:1202 for Docker Compose.
• Added a regression assertion to Docker runtime config tests.

Files changed

• docker-compose.yml
• .env.example
• backend/tests/test_docker_runtime_config.py
• README.md
• backend/README.md
• frontend/README.md
• docs/LOCAL_DEVELOPMENT_RUNBOOK.md
• CHANGELOG.md
• docs/CODEX_EXECUTION_LOG.md

Known limitations

• Docker commands still cannot be executed in this local Codex environment because Docker is unavailable here.

Pass 35 - Docker backend database startup retry (2026-06-16)

Completed

• Investigated Tower runtime logs showing backend Alembic startup failed with connection refused even after the db container reported healthy.
• Added backend/docker_start.sh to retry a real SQLAlchemy SELECT 1 connection before migrations.
• Updated Compose to run sh /app/docker_start.sh for backend startup.
• Added regression tests that require the Docker start script and SQL readiness retry before migrations.

Files changed

• backend/docker_start.sh
• docker-compose.yml
• backend/tests/test_docker_runtime_config.py
• backend/README.md
• CHANGELOG.md
• docs/CODEX_EXECUTION_LOG.md

Known limitations

• Docker commands still cannot be executed in this local Codex environment because Docker is unavailable here.

Pass 36 - Alembic Docker log formatting fix (2026-06-16)

Completed

• Investigated backend Docker logs showing repeated literal %(levelname)-5.5s [%(name)s] %(message)s lines during migrations.
• Fixed backend/alembic.ini logging formatter from escaped %%(...) tokens to runtime interpolation %(...) tokens.
• Added a regression test for Alembic logging formatter correctness.
• Verified Alembic SQL rendering no longer emits literal formatter spam.

Files changed

• backend/alembic.ini
• backend/tests/test_alembic_logging_config.py
• CHANGELOG.md
• docs/CODEX_EXECUTION_LOG.md

Pass 37 - Frontend same-origin API proxy for Docker LAN access (2026-06-16)

Completed

• Audited the running app at http://192.168.10.150:1202 from Codex: frontend HTML and backend /health were reachable, but /api/v1/projects on the frontend origin returned the frontend HTML fallback.
• Changed the frontend API client default from http://localhost:8000 to same-origin requests.
• Added Vite proxy routes for /api and /health.
• Added Docker Compose VITE_API_PROXY_TARGET=http://backend:8000 so LAN browsers use http://192.168.10.150:1202 only and the frontend container proxies API calls internally.
• Added regression tests for same-origin API/proxy behavior.

Files changed

• frontend/src/services/api/client.ts
• frontend/vite.config.ts
• docker-compose.yml
• backend/tests/test_docker_runtime_config.py
• README.md
• frontend/README.md
• CHANGELOG.md
• docs/CODEX_EXECUTION_LOG.md

Known limitations

• The running Tower deployment needs a rebuild/restart before this fix is active.

Pass 38 - Browser runtime proxy guard (2026-06-16)

Completed

• Added scripts/verify_browser_runtime.sh to verify the browser-facing frontend URL, /api/v1/projects proxy behavior and optional backend health endpoint.
• Added readiness syntax validation for the browser runtime verification script.
• Updated environment and local development documentation to prefer same-origin frontend API calls with Vite proxying in Docker/LAN deployments.

Files changed

• scripts/verify_browser_runtime.sh
• scripts/run_readiness_check.sh
• backend/tests/test_docker_runtime_config.py
• README.md
• docs/ENVIRONMENT_SPEC.md
• docs/LOCAL_DEVELOPMENT_RUNBOOK.md
• CHANGELOG.md
• docs/CODEX_EXECUTION_LOG.md

Known limitations

• The currently running Tower deployment at http://192.168.10.150:1202 still returns frontend HTML for /api/v1/projects until the frontend image is rebuilt and restarted.

Pass 39 - Environment contract cleanup (2026-06-16)

Completed

• Corrected .env.example and docs/ENVIRONMENT_SPEC.md to use the YOLO environment variable names read by backend settings: YOLO_ENABLED, YOLO_MODEL_PATH, YOLO_MODEL_VERSION and YOLO_MAX_TILES.
• Updated frontend example settings to keep VITE_API_BASE_URL empty by default and expose VITE_API_PROXY_TARGET for Vite proxy routing.
• Added regression coverage for example environment names and browser runtime proxy verification.

Files changed

• .env.example
• docs/ENVIRONMENT_SPEC.md
• backend/tests/test_docker_runtime_config.py
• CHANGELOG.md
• docs/CODEX_EXECUTION_LOG.md

Known limitations

• Docker cannot be executed in this local Codex environment; Tower still needs an image rebuild/restart to activate the frontend proxy fix.

Pass 40 - Frontend Docker nginx reverse proxy (2026-06-16)

Completed

• Replaced the Docker frontend runtime with a production Vite build served by nginx.
• Added frontend/nginx.conf with explicit reverse proxy rules for /api/ and /health to backend:8000.
• Changed Docker Compose frontend publishing from 1202:5173 to 1202:80.
• Updated Docker runtime regression tests and documentation so the browser-facing API path is no longer dependent on Vite dev-server proxy behavior.

Files changed

• frontend/Dockerfile
• frontend/nginx.conf
• docker-compose.yml
• backend/tests/test_docker_runtime_config.py
• README.md
• frontend/README.md
• docs/ENVIRONMENT_SPEC.md
• docs/LOCAL_DEVELOPMENT_RUNBOOK.md
• CHANGELOG.md
• docs/CODEX_EXECUTION_LOG.md

Known limitations

• Docker still cannot be executed in this local Codex environment. Tower must rebuild the frontend image to activate the nginx runtime.

Sprint 14 - Docker GIS runtime enablement (2026-06-16)

• Added backend gis optional dependency group for the approved Rasterio/GeoPandas runtime stack.
• Updated backend Docker image to install .[gis] and GDAL/GEOS/PROJ system packages.
• Added scripts/verify_gis_runtime.sh to verify PostGIS, Rasterio and GeoPandas capabilities through the browser-facing frontend proxy.
• Added readiness syntax coverage for the GIS runtime verification script.
• Added regression tests for Docker GIS dependency installation and capability verification coverage.
• Updated backend, environment, root README and changelog documentation with local/LAN verification commands.
• No API contracts, migrations, AI dependencies, provider fetching or product features were changed.
• Added scripts/gis_import_smoke.py and wired it into the backend Docker build so broken Rasterio/GeoPandas/pyogrio imports fail during image build.
• Added Docker Compose healthchecks for backend and frontend; frontend now waits for backend service health before starting.
• Corrected the GIS import smoke placement so the backend Docker build can access it inside the ./backend build context; the root script now wraps the backend script.

Sprint 15 - Explicit demo workflow seed (2026-06-16)

• Added POST /api/v1/demo/workflow for an explicit offline demo workflow seed.
• Added DemoWorkflowService to create or return a demo project, AOI, fixture reference dataset, fixture candidate dataset and persisted QA/QC metrics.
• Added scripts/seed_demo_workflow.py for terminal-based demo seeding.
• Added frontend Load demo workflow action in the Projects panel.
• Added endpoint/fixture-contract tests and readiness compile coverage for the demo seed script.
• No live GRB/OSM fetching, AI inference, migrations or new dependencies were introduced.

Sprint 16 - QA/QC result visibility (2026-06-16)

• Added GET /api/v1/projects/{project_id}/quality-checks for read-only project QA/QC result listing.
• Added QualityCheckService to return persisted quality_checks with metric rows.
• Added frontend QA/QC Results panel and API client support.
• Demo workflow loading and QA actions now refresh persisted QA/QC results in the UI.
• Added backend tests for quality check listing and canonical envelopes.
• No migrations, live provider fetching, AI inference or new dependencies were introduced.

Sprint 17 export foundation (2026-06-16)

Changed:

• Hardened GeoJSON exports so vector dataset, detection run and segmentation run exports persist exports rows and write JSON artifacts.
• Added project metadata JSON export plus export list/read/content endpoints.
• Added a frontend Export Center panel for creating exports, listing export records and previewing JSON content.
• Added backend tests for export persistence, artifact writing, raster rejection and canonical envelope behavior.

Tested:

• python -m compileall backend/app
• cd backend && python -m pytest -W error::DeprecationWarning
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh
• cd backend && python -m alembic heads && python -m alembic upgrade head --sql
• bash -n scripts/live_migration_smoke.sh && bash -n scripts/verify_browser_runtime.sh && bash -n scripts/verify_gis_runtime.sh

Open:

• Docker/live browser validation must be rerun on the deployment host after rebuild.
• YOLO-format export and report export remain documented future work; this pass only implements JSON/GeoJSON export foundation.

Limitations:

• Export artifacts are returned through API JSON content preview; browser file-download UX is not implemented yet.
• Detection and segmentation exports require existing persisted runs; no inference or fake output generation is introduced.

Next recommended pass:

• Rebuild/redeploy the Docker stack and verify /api/v1/exports/* through the LAN frontend proxy, then consider a lightweight file-download endpoint or report artifact pass.

Sprint 17 export download hardening (2026-06-16)

Changed:

• Added GET /api/v1/exports/{export_id}/download as a raw file response for stored JSON/GeoJSON export artifacts.
• Reused the same export artifact existence validation for content preview and downloads.
• Added frontend Export Center download buttons using the configured/same-origin API base URL.
• Added backend tests for missing artifacts and file download response headers/content.

Tested:

• python -m compileall backend/app
• cd backend && python -m pytest -W error::DeprecationWarning
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh
• cd backend && python -m alembic heads && python -m alembic upgrade head --sql
• bash -n scripts/live_migration_smoke.sh && bash -n scripts/verify_browser_runtime.sh && bash -n scripts/verify_gis_runtime.sh

Open:

• Docker/LAN validation should be rerun after redeploy on the Tower host.

Limitations:

• Download endpoint is intentionally a raw file response, not a canonical JSON envelope, because it is a browser/file artifact path.

Next recommended pass:

• Rebuild Docker and verify Export Center create/preview/download against http://192.168.10.150:1202.

Sprint 17 lightweight report artifact export (2026-06-16)

Changed:

• Added POST /api/v1/exports/report to create a lightweight HTML project report artifact from persisted project, dataset and QA/QC summary state.
• Added HTML escaping for report-rendered project and dataset values.
• Updated export downloads to return text/html for HTML report artifacts and application/json for JSON/GeoJSON artifacts.
• Added a frontend Export Center action for project report HTML export.
• Added backend tests for HTML report artifact creation and HTML download response behavior.

Tested:

• python -m compileall backend/app
• cd backend && python -m pytest -W error::DeprecationWarning
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh
• cd backend && python -m alembic heads && python -m alembic upgrade head --sql
• bash -n scripts/live_migration_smoke.sh && bash -n scripts/verify_browser_runtime.sh && bash -n scripts/verify_gis_runtime.sh

Open:

• Docker/LAN verification should be rerun after deployment rebuild.

Limitations:

• Report export is intentionally a simple HTML artifact, not a PDF designer or standalone Reports module.
• Report content is summary-only and uses existing persisted project, dataset and QA/QC rows.

Next recommended pass:

• Rebuild Docker and verify Export Center metadata, GeoJSON, report HTML and download flows through the LAN URL.

Sprint 17 export audit trail and LAN demo/export smoke (2026-06-16)

Changed:

• Added export history to project metadata JSON and lightweight HTML report artifacts.
• Added scripts/verify_demo_export_workflow.sh to verify the browser-facing demo workflow, persisted QA/QC listing, metadata export, report export, vector GeoJSON export, export listing and artifact downloads.
• Included the demo/export workflow script in the main readiness syntax gate.
• Added backend tests to lock export-history content and demo/export script coverage.

Tested:

• python -m compileall backend/app
• cd backend && python -m pytest -W error::DeprecationWarning
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh
• cd backend && python -m alembic heads && python -m alembic upgrade head --sql
• bash -n scripts/live_migration_smoke.sh && bash -n scripts/verify_browser_runtime.sh && bash -n scripts/verify_gis_runtime.sh && bash -n scripts/verify_demo_export_workflow.sh

Open:

• scripts/verify_demo_export_workflow.sh still needs to be run against the rebuilt Tower deployment URL.

Limitations:

• The smoke script intentionally uses explicit fixture demo data and does not fetch live GRB/OSM or run AI inference.

Next recommended pass:

• Rebuild Docker on the Tower host and run bash scripts/verify_demo_export_workflow.sh http://192.168.10.150:1202.

Sprint 32 Unraid all-in-one runtime verification (2026-06-17)

Changed:

• Converted the Unraid/Tower runtime to a single editable geointel Docker container via docker-compose.unraid.yml.
• Built the all-in-one image from postgres:16-bookworm with PostgreSQL 16/PostGIS packages, the FastAPI backend, nginx and the React frontend.
• Removed the default nginx site from the image so /api/v1/* is proxied to the embedded backend instead of returning nginx 404s.
• Hardened live migration smoke and browser runtime verification with startup retries.
• Browser runtime verification now checks the frontend, /api/v1/projects canonical JSON envelope and /geointel-icon.png.

Tested:

• python -m pytest backend\tests\test_sprint31_unraid_template.py
• bash -n deploy/unraid/all-in-one-start.sh
• bash -n scripts/live_migration_smoke.sh
• bash -n scripts/verify_browser_runtime.sh
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts\deploy_tower.ps1
• docker compose -f docker-compose.unraid.yml ps on Tower
• bash scripts/verify_browser_runtime.sh http://192.168.10.150:1202

Verified runtime:

• Tower runs one GeoIntel app container: geointel.
• Published browser port is 0.0.0.0:1202->80/tcp.
• Container health is healthy.
• Live migration smoke passed with PostGIS available and one Alembic head.
• Frontend, API proxy and icon are reachable at http://192.168.10.150:1202.

Open:

• Existing reused PostgreSQL volume logs a collation version mismatch because the old database was created on Debian glibc 2.31 and the all-in-one runtime uses glibc 2.36. Runtime and migrations pass; a future maintenance pass can rebuild/refresh collation metadata if needed.

Sprint 32 DockerMan template/icon follow-up (2026-06-17)

Changed:

• Added a PNG icon for Unraid/DockerMan because DockerMan may not reliably render app-served SVG icons.
• Changed the Unraid template <Name> to geointel so it matches the running all-in-one container name.
• Changed the template icon URL to the absolute PNG URL http://192.168.10.150:1202/geointel-icon.png.
• Added DockerMan labels to docker-compose.unraid.yml so the running Compose container exposes Unraid managed/webui/icon metadata.
• Updated Tower deploy scripts to copy the editable template to /boot/config/plugins/dockerMan/templates-user/my-geointel.xml.
• Updated Tower deploy scripts to copy the PNG icon to /boot/config/plugins/dockerMan/images/geointel-icon.png.

Expected Unraid behavior:

• Refresh the Unraid Docker page after deploy so DockerMan reloads the user template metadata.
• The running geointel container should have template-backed editable fields and a PNG icon.

Sprint 32 automatic DockerMan-native deploy follow-up (2026-06-17)

Changed:

• Added deploy/unraid/run-dockerman-container.sh as the single source of truth for the final Unraid container launch.
• Updated Tower deploy scripts so repository deploys validate the Compose reference, build the image with plain docker build and start the final container with docker run plus DockerMan labels.
• The launch script installs the DockerMan template/icon, removes old geointel containers, preserves/migrates the old Compose PostGIS volume when needed and starts the final geointel container as DockerMan-managed.
• Updated live migration smoke to support direct container execution through LIVE_SMOKE_CONTAINER=geointel.

Expected Unraid behavior:

• A deploy from the repo should no longer leave the final app as a plain Compose-owned container.
• The final image/container should avoid Compose metadata labels that can confuse Unraid's Docker page.
• The running geointel container should expose net.unraid.docker.managed=dockerman, web UI metadata and icon metadata immediately after deploy.

Sprint 33 QA/QC benchmark readiness hardening (2026-06-17)

Changed:

• Added scripts/verify_golden_qa_benchmark.sh as a shell wrapper for the deterministic QA/QC golden benchmark.
• Made scripts/run_readiness_check.sh run scripts/run_golden_qa_benchmark.py --json so QA/QC metric drift fails the main release gate.
• Added a readiness syntax check for the golden benchmark wrapper.
• Hardened scripts/validate_fixtures.py so fixtures/golden GeoJSON files and expected fixture paths are validated alongside the general GeoJSON fixtures.
• Added backend regression tests that keep the golden benchmark wired into readiness.
• Updated script/backend docs, TODO and changelog.

Tested:

• python scripts/validate_fixtures.py
• python scripts/run_golden_qa_benchmark.py --json
• bash -n scripts/verify_golden_qa_benchmark.sh
• bash scripts/verify_golden_qa_benchmark.sh
• python -m compileall backend/app
• cd backend && python -m pytest -W error::DeprecationWarning
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh
• cd backend && python -m alembic heads && python -m alembic upgrade head --sql
• bash -n scripts/live_migration_smoke.sh

Open:

• None for this pass.

Limitations:

• The benchmark intentionally uses explicit local fixtures only. It does not fetch live GRB/OSM data and does not run AI inference.

Next recommended pass:

• Continue with broader QA/QC golden demo coverage or frontend export preview decomposition.

Sprint 34 browser-facing golden QA demo hardening (2026-06-17)

Changed:

• Hardened scripts/verify_demo_export_workflow.sh so the browser-facing demo/export smoke loads fixtures/golden/expected_qa_metrics.json.
• The smoke now verifies persisted QA/QC status, F1 score, precision, recall, mean IoU, false positives, false negatives and match counts against the golden baseline.
• Corrected the offline demo AOI to cover the golden fixture geometries instead of an older broad Kempen placeholder outside the fixture coordinates.
• Made existing demo workflows self-heal stale/unsupported QA checks by syncing the demo AOI and persisting a fresh golden QA/QC result.
• Added regression checks in backend tests so the demo/export smoke cannot regress back to key-existence-only QA/QC validation.
• Updated script documentation, TODO and changelog.

Tested:

• python -m compileall backend/app
• cd backend && python -m pytest tests/test_sprint15_demo_workflow.py tests/test_sprint21_demo_workflow_smoke.py tests/test_readiness_gate.py -q
• cd backend && python -m pytest -W error::DeprecationWarning
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1
• bash scripts/verify_browser_runtime.sh http://192.168.10.150:1202
• bash scripts/verify_gis_runtime.sh http://192.168.10.150:1202
• bash scripts/verify_demo_export_workflow.sh http://192.168.10.150:1202

Open:

• Docker build still emits a warning about GEOINTEL_POSTGRES_PASSWORD being present as an image ENV; runtime behavior is green, but a future secret-hygiene pass should move that default out of the Dockerfile.

Limitations:

• The demo/export smoke remains intentionally fixture-based and idempotent. It does not fetch live providers and does not run AI inference.

Next recommended pass:

• Run the full release-readiness gate and then rebuild/deploy to Tower for browser-facing verification.

Sprint 35 Docker runtime secret hygiene (2026-06-17)

Changed:

• Removed embedded PostGIS database name/user/password defaults from deploy/unraid/Dockerfile.all-in-one image metadata.
• Kept database credentials as runtime configuration supplied by .env, the Unraid template, Compose or docker run -e.
• Added regression coverage so GEOINTEL_POSTGRES_PASSWORD is not baked into the all-in-one Dockerfile again.
• Updated Unraid runtime documentation and changelog.

Tested:

• cd backend && python -m pytest tests/test_sprint31_unraid_template.py tests/test_docker_runtime_config.py -q
• bash -n deploy/unraid/all-in-one-start.sh
• bash -n deploy/unraid/run-dockerman-container.sh
• python -m compileall backend/app
• cd backend && python -m pytest -W error::DeprecationWarning
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh
• cd backend && python -m alembic heads && python -m alembic upgrade head --sql
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1
• bash scripts/verify_browser_runtime.sh http://192.168.10.150:1202
• bash scripts/verify_gis_runtime.sh http://192.168.10.150:1202
• bash scripts/verify_demo_export_workflow.sh http://192.168.10.150:1202

Open:

• Local Windows shell has no docker command in PATH, so Docker build/config verification must run on Tower during deploy.
• Tower rebuild completed and the previous Docker BuildKit SecretsUsedInArgOrEnv warning no longer appears.

Limitations:

• The start script still has safe local defaults for standalone/dev startup. Shared deployments should set GEOINTEL_POSTGRES_PASSWORD via runtime configuration.

Next recommended pass:

• Run release readiness, rebuild/deploy Tower and verify browser/GIS/demo smoke again.

Sprint 36 PostgreSQL collation maintenance visibility (2026-06-17)

Changed:

• Added PostgreSQL database collation version reporting to scripts/live_migration_smoke.sh.
• The live smoke now prints COLLATION_VERSION_MISMATCH with stored and actual versions when a reused PostGIS volume was created under an older libc/collation runtime.
• The smoke also prints the exact ALTER DATABASE "... " REFRESH COLLATION VERSION; acknowledgement command, but does not run it automatically.
• Documented the Unraid maintenance procedure and backup/index review guidance.
• Added regression coverage for the collation reporting path.

Tested:

• cd backend && python -m pytest tests/test_live_migration_smoke_script.py -q
• bash -n scripts/live_migration_smoke.sh
• python -m compileall backend/app
• cd backend && python -m pytest -W error::DeprecationWarning
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1
• bash scripts/verify_browser_runtime.sh http://192.168.10.150:1202
• bash scripts/verify_gis_runtime.sh http://192.168.10.150:1202
• bash scripts/verify_demo_export_workflow.sh http://192.168.10.150:1202

Open:

• Tower live smoke reports COLLATION_VERSION_MISMATCH: database=geointel stored=2.31 actual=2.36.
• Runtime remains green. The next maintenance action is optional/manual acknowledgement after backup/index review: ALTER DATABASE "geointel" REFRESH COLLATION VERSION;.

Limitations:

• The smoke reports and documents the maintenance action. It intentionally does not mutate collation metadata automatically.

Next recommended pass:

• Run release readiness, deploy Tower and decide whether to manually acknowledge the current collation version.

Sprint 49 Workbench shell UI refactor (2026-06-17)

Changed:

• Audited the live workbench UI and confirmed the main usability issue was information architecture: all V1 workflows were mounted as one long vertical panel stack.
• Refactored the frontend into a task-based workbench shell with Overview, Data, Map, QA/QC, AI Labs, Exports and System workspaces.
• Added a persistent top context bar for active project, AOI, dataset and layer state.
• Moved selected dataset details into a persistent right-side inspector while keeping the same dataset/raster/vector operation callbacks.
• Added stable primary navigation test anchors.
• Documented the new frontend shell structure and updated the changelog.

Tested:

• cd frontend && npm run typecheck
• cd frontend && npm run build
• Local Vite visual audit on http://127.0.0.1:5175

Open:

• Local Vite visual audit shows Request failed (404) when no local backend/proxy target is available. Docker/nginx same-origin proxy behavior remains the production path.
• The next pass should run full release readiness and deploy to Tower for browser-facing verification on http://192.168.10.150:1202.

Limitations:

• This pass intentionally changes UI structure only. It does not add product capabilities, alter API contracts, change migrations, fetch live providers or enable new AI models.

Next recommended pass:

• Run full readiness, rebuild/deploy Tower, then perform a live browser smoke through the new workbench navigation.

Sprint 50 Workspace usability polish (2026-06-17)

Changed:

• Refined the task-based shell workspaces after the first UI refactor.
• Converted Project, AOI and Dataset panels into compact forms and card-based lists for faster scanning.
• Converted the Map workspace controls into a toolbar with dedicated AOI/layer controls and status.
• Converted Detection Lab and Segmentation Lab into model, run, result and QA blocks.
• Added CSS utilities for entity cards, dataset cards, model cards, lab blocks, primary/secondary actions and responsive nested forms.
• Added regression coverage for the polished workspace structure.

Tested:

• cd frontend && npm run typecheck
• cd frontend && npm run build
• cd backend && python -m pytest tests/test_sprint47_workbench_interaction_smoke.py tests/test_sprint49_workbench_shell_refactor.py -q

Open:

• Run full release readiness and deploy the polish to Tower.

Limitations:

• This pass remains frontend-only. It does not add features, change API contracts, alter migrations, fetch live providers or enable new AI models.

Next recommended pass:

• Deploy to Tower and verify the Data, Map and AI Labs workspaces on http://192.168.10.150:1202.

Sprint 51 QA/QC and export workspace polish (2026-06-17)

Changed:

• Refined the QA/QC workspace so persisted checks are shown as summary tiles, quality-check cards and metric chips instead of a raw nested list.
• Refined the Exports workspace with grouped export actions, latest-export status, artifact history cards and a framed JSON/GeoJSON preview panel.
• Kept the existing useQualityWorkflow and useExportWorkflow dataflow intact; no API client calls or backend contracts changed.
• Added regression coverage for the polished QA/QC and Exports workspace structure.

Tested:

• cd frontend && npm run typecheck
• cd backend && python -m pytest tests/test_sprint27_frontend_workflow_hooks.py tests/test_sprint30_workbench_components.py tests/test_sprint47_workbench_interaction_smoke.py -q
• cd backend && python -m pytest tests/test_sprint51_quality_export_polish.py -q
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh (207 passed)
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1
• Tower live migration smoke passed.
• Tower browser runtime verification passed on http://192.168.10.150:1202.

Open:

• Manual visual review of populated QA/QC and Exports states with real project artifacts remains useful after the next demo workflow run.

Limitations:

• This pass remains UI polish only. It does not add product capabilities, change API contracts, alter migrations, fetch live providers or enable new AI models.

Next recommended pass:

• Verify QA/QC and Exports on the deployed Tower workbench, then continue with selected-object inspector detail tabs.

Sprint 52 selected context inspector tabs (2026-06-17)

Changed:

• Replaced the dataset-only right inspector with a tabbed WorkbenchInspector.
• Added Context, Dataset, QA/Exports and AI Runs tabs using existing project, AOI, dataset, QA, export and AI-run state.
• Kept the existing DatasetDetailPanel as the Dataset tab so raster/vector operations and callbacks remain behavior-compatible.
• Added inspector cards for selected map feature properties, latest QA/QC, latest export and selected detection/segmentation run summaries.
• Added regression coverage for inspector wiring and tab structure.

Tested:

• cd frontend && npm run typecheck
• cd backend && python -m pytest tests/test_sprint49_workbench_shell_refactor.py tests/test_sprint50_workspace_usability_polish.py tests/test_sprint51_quality_export_polish.py -q
• cd backend && python -m pytest tests/test_sprint28_dataset_workflow_hook.py tests/test_sprint29_dataset_components.py tests/test_sprint52_workbench_inspector_tabs.py -q
• cd backend && python -m pytest tests/test_sprint52_workbench_inspector_tabs.py -q
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh (210 passed)
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1
• Tower live migration smoke passed.
• Tower browser runtime verification passed on http://192.168.10.150:1202.

Open:

• Manual click-through of each inspector tab with populated demo data remains useful for visual fine-tuning.

Limitations:

• This pass remains UI orchestration only. It does not add features, change API contracts, alter migrations, fetch live providers or enable new AI models.

Next recommended pass:

• Verify the tabbed inspector live on Tower, then continue with map/dataset selection ergonomics.

Sprint 53 map/dataset selection ergonomics (2026-06-17)

Changed:

• Added active-state styling to dataset cards so the selected dataset is visibly anchored in the catalog.
• Added dataset quick actions to open a dataset directly in the Map workspace or Exports workspace after loading details.
• Added inspector navigation actions to jump to Data, Map, QA/QC, Exports and AI Labs without hunting through the left navigation.
• Kept the existing dataset loading, map layer state, export flow and API clients unchanged.
• Added regression coverage for selection quick actions and inspector navigation wiring.

Tested:

• cd frontend && npm run typecheck
• cd backend && python -m pytest tests/test_sprint28_dataset_workflow_hook.py tests/test_sprint29_dataset_components.py tests/test_sprint52_workbench_inspector_tabs.py -q
• cd backend && python -m pytest tests/test_sprint53_selection_ergonomics.py -q
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh (213 passed)
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1
• Tower live migration smoke passed.
• Tower browser runtime verification passed on http://192.168.10.150:1202.

Open:

• Manual demo-data click-through can still tune copy/density after real populated cards are visible.

Limitations:

• This pass remains UI orchestration only. It does not add features, change API contracts, alter migrations, fetch live providers or enable new AI models.

Next recommended pass:

• Verify dataset quick actions with demo data on Tower, then improve populated map/detail readability if needed.

Sprint 54 populated-state UI polish (2026-06-17)

Changed:

• Ran the live demo/export workflow against the Tower deployment to inspect real populated workbench states.
• Adjusted the Data workspace so Project and AOI remain side by side while the Dataset catalog spans the full row for readable populated dataset cards.
• Made dataset-card action rows responsive so Map/Exports/detail actions do not crowd or clip on populated cards.
• Limited the Exports artifact history to the latest 10 entries by default with an explicit show-all toggle.
• Kept the Export Preview panel visible even before an artifact is selected, avoiding a blank middle column in the Exports workspace.
• Shortened displayed export storage paths while keeping the full path available in the title attribute.
• Added regression coverage for the populated Data layout and Exports populated-state behavior.

Tested:

• bash scripts/verify_demo_export_workflow.sh http://192.168.10.150:1202
• Local Vite visual audit using the live Tower API proxy on http://127.0.0.1:5176
• cd frontend && npm run typecheck
• cd backend && python -m pytest tests/test_sprint51_quality_export_polish.py tests/test_sprint53_selection_ergonomics.py -q
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh (214 passed)
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1
• Tower live migration smoke passed.
• Tower browser runtime verification passed on http://192.168.10.150:1202.
• Direct HTTP check passed for http://192.168.10.150:1202 and http://192.168.10.150:1202/api/v1/projects.

Open:

• Continue monitoring export history growth during repeated demo workflow runs.

Limitations:

• This pass remains UI polish only. It does not add features, change API contracts, alter migrations, fetch live providers or enable new AI models.

Next recommended pass:

• Add export history filtering or retention controls if artifact history continues to grow during demo runs.

Sprint 55 live visual shell polish (2026-06-17)

Changed:

• Performed a Browser-based visual audit of the live Tower workbench at http://192.168.10.150:1202.
• Confirmed the main visual defect was the cramped three-column desktop shell: left navigation, central workspace and inspector competed for width at 1280px.
• Compacted the sticky top context bar and left navigation.
• Changed the standard desktop breakpoint so the inspector moves below the workspace up to 1360px, while remaining a side panel on wider displays.
• Made the Map toolbar wrap responsively instead of forcing four controls into a narrow row.
• Added a workspace-change scroll reset so switching pages starts at the workspace heading instead of inheriting stale scroll position.
• Added regression coverage for the standard desktop shell width and scroll-reset behavior.

Tested:

• Local Browser visual audit against http://127.0.0.1:5177 using the live Tower API proxy.
• Verified Overview, Data, Map and Exports workspaces visually after the shell changes.
• Browser console error/warning check returned no entries.
• cd frontend && npm run typecheck
• cd backend && python -m pytest tests/test_sprint53_selection_ergonomics.py tests/test_sprint50_workspace_usability_polish.py -q
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh (215 passed)
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1
• Tower live migration smoke passed.
• Tower browser runtime verification passed on http://192.168.10.150:1202.
• Live Browser visual verification passed for Overview and Map on http://192.168.10.150:1202.

Open:

• Export history still grows during repeated demo runs and should get filtering or retention controls.

Limitations:

• This pass remains UI shell polish only. It does not add features, change API contracts, alter migrations, fetch live providers or enable new AI models.

Next recommended pass:

• Continue with export history filtering or retention controls if repeated demo runs keep growing artifact history.

Sprint 56 export history controls (2026-06-17)

Changed:

• Added frontend-only search for export type, status, id and storage path in the Exports workspace.
• Added export type and status filters generated from the loaded export records.
• Made the latest-10 limiter apply after filtering so large histories stay manageable without hiding matching records unexpectedly.
• Added a clear no-match empty state and reset view action.
• Slightly compacted export action buttons so the history controls are visible earlier on standard desktop viewports.
• Added regression coverage for the export history controls, filtered list limiting and no-match state.

Tested:

• cd frontend && npm run typecheck
• cd backend && python -m pytest tests/test_sprint51_quality_export_polish.py -q
• cd frontend && npm run build
• Local Browser visual audit against http://127.0.0.1:5178 using the live Tower API proxy.
• Verified Exports history controls render with 50 live artifacts and a report search narrows the list to 17 matching artifacts.
• Browser console error/warning check returned no entries.

Open:

• Run full readiness, deploy Tower and verify the live Exports filters on http://192.168.10.150:1202.

Limitations:

• This pass remains frontend UI hardening only. It does not add backend filtering, retention deletion, API changes, migrations, live provider fetching or AI model behavior.

Next recommended pass:

• Consider a safe export retention/cleanup command if the artifact table keeps growing beyond demo needs.

Sprint 57 safer demo export cleanup (2026-06-17)

Changed:

• Hardened the existing dry-run-first demo export cleanup command instead of creating a parallel cleanup path.
• Added --max-delete with a default cap of 25 so large --apply runs are blocked until explicitly reviewed and raised.
• Added repeatable --export-type filters for targeted cleanup, e.g. reports only.
• Extended the cleanup summary with keep_latest, max_delete, export_types, type_filtered_export_count, candidate_exports and blocked_reason.
• Updated the root wrapper to expose the new filter helper.
• Updated scripts/README.md, docs/STORAGE_ARCHITECTURE.md, backend/README.md, docs/TODO.md and CHANGELOG.md.
• Added regression coverage for export-type filtering, parser defaults and max-delete options.

Tested:

• python -m py_compile scripts/cleanup_demo_artifacts.py backend/scripts/cleanup_demo_artifacts.py
• cd backend && python -m pytest tests/test_sprint24_cleanup_demo_artifacts.py tests/test_readiness_gate.py -q (16 passed)
• Tower dry-run command passed without deleting data: docker exec geointel /opt/geointel/venv/bin/python /app/scripts/cleanup_demo_artifacts.py --keep-latest 10 --max-delete 100 --export-type project_report_html
• Tower dry-run reported matched_export_count=51, type_filtered_export_count=17, selected_export_count=7, deleted_export_count=0.

Open:

• Run full readiness and redeploy Tower with the clearer candidate_exports dry-run output.

Limitations:

• Cleanup still targets demo export records/files only. It does not delete source uploads, vector features, projects, AOIs, QA/QC records, rasters, tiles, masks or production data.

Next recommended pass:

• Add a small live maintenance smoke that runs cleanup in dry-run mode through the deployed all-in-one container.

Sprint 58 demo cleanup dry-run smoke (2026-06-18)

Changed:

• Added scripts/verify_demo_cleanup_dry_run.sh as a live maintenance smoke for the demo export cleanup path.
• The smoke auto-detects a running compose backend or all-in-one geointel container, with explicit CLEANUP_MODE=local|compose|container overrides.
• The smoke runs cleanup without --apply, then verifies dry_run=true, deleted_export_count=0, deleted_files=[], expected filter settings and candidate dry-run fields.
• Added the smoke syntax check to scripts/run_readiness_check.sh.
• Added regression coverage that the readiness gate checks the smoke and that the smoke contract remains non-mutating.
• Updated scripts/README.md, docs/STORAGE_ARCHITECTURE.md, backend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• bash -n scripts/verify_demo_cleanup_dry_run.sh
• python -m py_compile scripts/cleanup_demo_artifacts.py backend/scripts/cleanup_demo_artifacts.py
• cd backend && python -m pytest tests/test_readiness_gate.py tests/test_sprint24_cleanup_demo_artifacts.py -q (18 passed)
• bash scripts/run_readiness_check.sh (219 passed)
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1
• Tower live migration smoke passed.
• Tower browser runtime verification passed on http://192.168.10.150:1202.
• Tower cleanup dry-run smoke passed with matched=51, type_filtered=17, selected=7, deleted=0.

Open:

• None for this pass.

Limitations:

• The readiness gate checks this live smoke's syntax only. The actual cleanup dry-run still requires a running backend/PostGIS runtime.

Next recommended pass:

• Add browser screenshot artifact automation for visual regression handoff.

Sprint 59 workbench screenshot artifacts (2026-06-18)

Changed:

• Added scripts/capture_workbench_screenshots.sh for optional visual regression handoff screenshots.
• The script seeds the explicit offline demo workflow, opens Overview, Data, Map, QA/QC, AI Labs, Exports and System, then writes viewport screenshots plus manifest.json.
• Desktop capture is always enabled; mobile capture is enabled by default and can be disabled with CAPTURE_MOBILE=0.
• Added /artifacts/ to .gitignore so screenshot outputs stay local.
• Added readiness syntax coverage and regression checks for the screenshot capture contract.
• Updated scripts/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• bash -n scripts/capture_workbench_screenshots.sh
• cd backend && python -m pytest tests/test_readiness_gate.py -q (13 passed)
• bash scripts/run_readiness_check.sh (221 passed)
• Local script execution without Playwright fails cleanly with setup instructions instead of producing partial artifacts.
• Browser-plugin visual capture against http://192.168.10.150:1202 created viewport screenshots for all seven workspaces with no console errors or warnings.

Open:

• Commit, deploy Tower and keep the visual artifact script available for future Playwright-enabled runners.

Limitations:

• Playwright/Chromium is intentionally optional and not added to the frontend dependency set. The readiness gate checks script syntax only.

Next recommended pass:

• Run a backend error-envelope audit for expected user-error paths.

Sprint 60 API error-envelope contract hardening (2026-06-18)

Changed:

• Audited the backend error payload shape against docs/API_CONTRACTS.md.
• Changed central FastAPI error serialization to return top-level error, message, details and request_id fields.
• Preserved HTTPException detail dict support for future explicit error codes.
• Updated the frontend API client to parse the canonical top-level error contract while remaining tolerant of the older nested error.code/message/details shape.
• Added regression tests for AppError, HTTPException and validation-error envelopes.
• Updated provider-registry error assertions to the canonical top-level schema.
• Added static frontend parser coverage for canonical and legacy error payloads.
• Updated docs/TODO.md and CHANGELOG.md.

Tested:

• cd backend && python -m pytest tests/test_error_envelope_contract.py tests/test_sprint7b_provider_registry.py::test_provider_api_envelopes_and_invalid_provider -q (4 passed)
• python -m compileall backend/app
• cd frontend && npm run typecheck
• cd backend && python -m pytest -q (224 passed)
• cd frontend && npm run build
• cd backend && python -m pytest tests/test_error_envelope_contract.py tests/test_frontend_api_client_error_parser.py tests/test_sprint7b_provider_registry.py::test_provider_api_envelopes_and_invalid_provider -q (6 passed)
• bash scripts/run_readiness_check.sh (226 passed)
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1
• Tower live migration smoke passed.
• Tower browser runtime verification passed on http://192.168.10.150:1202.
• Live GET /api/v1/external/providers/unknown returned {"error":"PROVIDER_NOT_FOUND","message":"Provider not found","details":{},"request_id":null}.
• Live GET /api/v1/projects/not-a-uuid returned top-level VALIDATION_ERROR.

Open:

• None for this pass.

Limitations:

• This pass changes only the centralized response envelope and frontend parser. It does not rename route-level error codes or change product behavior.

Next recommended pass:

• Expand golden datasets beyond the current building QA fixtures.

Sprint 61 Golden QA scenario expansion (2026-06-18)

Changed:

• Added fixtures/golden/golden_qa_benchmarks.json as the explicit scenario manifest for deterministic QA/QC regression coverage.
• Added local golden fixture pairs for perfect-match, no-overlap and MultiPolygon building comparisons.
• Updated scripts/run_golden_qa_benchmark.py to execute all manifest scenarios, assert expected metric drift, persist simulated QualityCheck/Metric rows for each scenario and report aggregate persistence totals.
• Preserved backward-compatible top-level benchmark fields for scripts that still read the original single-scenario output shape.
• Updated scripts/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Red step: cd backend && python -m pytest tests/test_sprint12_golden_qa_benchmark.py -q failed on missing multi-scenario manifest/output.
• cd backend && python -m pytest tests/test_sprint12_golden_qa_benchmark.py -q (4 passed)
• python scripts/run_golden_qa_benchmark.py --json

Open:

• Run full readiness and deploy Tower after the expanded golden benchmark passes the release gate.

Limitations:

• These are deterministic local QA/QC fixtures only. They do not introduce new production QA metrics or provider data.

Next recommended pass:

• Continue with frontend visual polish backlog or add a live golden benchmark smoke only if a running PostGIS environment needs that extra release signal.

Sprint 62 Workbench visual polish (2026-06-18)

Changed:

• Added a compact workspace command bar under the active workspace heading for quick movement between the main workbench surfaces.
• Polished the shared frontend visual system with raised/sunken surface tokens, softer shadows, tighter topbar spacing and consistent workspace panel styling.
• Replaced raw project/dataset empty-state text with structured empty-state blocks.
• Wrapped Detection Lab and Segmentation Lab result summaries in scan-friendly result cards and long result tables in scroll-safe containers.
• Improved mobile navigation density by making the primary nav and command chips horizontal rails on narrow screens.
• Added backend/tests/test_sprint62_frontend_visual_polish.py to guard the visual polish contracts.
• Updated frontend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Red step: cd backend && python -m pytest tests/test_sprint62_frontend_visual_polish.py -q failed on missing command bar, panel polish and empty/result wrappers.
• cd backend && python -m pytest tests/test_sprint62_frontend_visual_polish.py tests/test_sprint49_workbench_shell_refactor.py tests/test_sprint50_workspace_usability_polish.py -q (6 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• Local Chrome/Playwright visual check against http://127.0.0.1:5175 with live API proxy: desktop and mobile screenshots captured, no console warnings/errors, no horizontal page overflow.

Open:

• Run full readiness and deploy Tower after this visual polish pass.

Limitations:

• This is a frontend usability polish pass only. It does not add new API behavior, data processing, provider fetching or AI model functionality.

Next recommended pass:

• Continue with map/result overlay ergonomics, especially making selected dataset/result provenance easier to see beside the map.

Sprint 63 Map overlay ergonomics (2026-06-18)

Changed:

• Added active map layer source/provenance/draw-state context in the Map workspace using existing selected dataset, detection, segmentation and change-detection frontend state.
• Added a clear map empty-state when no vector/result layer is active.
• Added selected-feature property chips before the raw JSON feature inspector so common properties are scan-friendly.
• Tightened panel-title alignment with a scoped CSS override after the broader visual polish exposed a specificity issue in the existing section > div:not(...) rule.
• Added backend/tests/test_sprint63_map_overlay_ergonomics.py.
• Updated frontend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Red step: cd backend && python -m pytest tests/test_sprint63_map_overlay_ergonomics.py -q failed on missing map provenance/feature-summary UI.
• cd backend && python -m pytest tests/test_sprint63_map_overlay_ergonomics.py tests/test_sprint30_workbench_components.py tests/test_sprint53_selection_ergonomics.py -q (11 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• Local Chrome/Playwright check against http://127.0.0.1:5175 with live API proxy: Map workspace opened, provenance rail present, no console warnings/errors, no horizontal page overflow.

Open:

• Run full readiness and deploy Tower after this pass.

Limitations:

• This is a frontend ergonomics pass only. It does not add new map layers, backend endpoints, provider fetching or geospatial processing.

Next recommended pass:

• Continue with export/report handoff polish or add a live browser smoke that explicitly visits every workspace after deployment.

Sprint 64 Export/report handoff polish (2026-06-18)

Changed:

• Added a handoff readiness card to the Export Center using existing project, selected dataset, selected detection run, selected segmentation run and latest export state.
• Reworked existing export actions into artifact cards for refresh, project report, project metadata, selected vector GeoJSON, detection GeoJSON and segmentation GeoJSON.
• Added formatted export-type badges and extra export-card provenance for analysis-run ids and created timestamps when those fields are available.
• Added responsive styling for handoff summary/action cards.
• Added backend/tests/test_sprint64_export_handoff_polish.py.
• Updated frontend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Red step: python -m pytest backend/tests/test_sprint64_export_handoff_polish.py -q failed on missing handoff summary/action structure and styles.
• python -m pytest backend/tests/test_sprint64_export_handoff_polish.py backend/tests/test_sprint51_quality_export_polish.py -q (4 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• Local Chrome/Playwright check against http://127.0.0.1:5175 with live API proxy: Exports workspace opened, handoff summary/actions present, no console warnings/errors, no horizontal page overflow on desktop or mobile.
• bash scripts/run_readiness_check.sh (233 passed)
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1 rebuilt and redeployed the all-in-one container on Tower, with live migration smoke and browser runtime verification passing.
• Live Chrome/Playwright check against http://192.168.10.150:1202: Exports workspace opened, 6 handoff action cards present, no console warnings/errors, no horizontal page overflow on desktop or mobile.

Open:

• None for this pass.

Limitations:

• This is a frontend handoff usability pass only. It does not add export endpoints, change export persistence, introduce provider fetching or add AI/model behavior.

Next recommended pass:

• Run a live browser smoke across Exports and Overview after deployment, then continue with report artifact readability if the exported HTML itself needs visual polish.

Sprint 65 Project report readability polish (2026-06-18)

Changed:

• Reworked the lightweight project_report_html renderer into a self-contained handoff layout with hero, readiness pill, scorecards and sectioned report content.
• Added print-friendly CSS and scroll-safe table wrappers to the HTML artifact.
• Added source and CRS columns to the dataset inventory section.
• Preserved existing export type, endpoint behavior, download behavior and storage flow.
• Added backend/tests/test_sprint65_project_report_polish.py.
• Updated backend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Red step: python -m pytest backend/tests/test_sprint65_project_report_polish.py -q failed on missing report shell, scorecards, print styles and readiness pill classes.
• python -m pytest backend/tests/test_sprint65_project_report_polish.py backend/tests/test_sprint17_export_foundation.py -q (12 passed)
• python -m compileall backend/app
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh (235 passed)
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1 rebuilt and redeployed the Tower all-in-one container, with live migration smoke and browser runtime verification passing.
• Live report smoke through http://192.168.10.150:1202: seeded explicit demo workflow, created project_report_html, downloaded the HTML artifact and verified it in Chrome with 5 scorecards, 5 report sections, no console warnings/errors and no horizontal overflow on desktop or mobile.

Open:

• None for this pass.

Limitations:

• This remains a lightweight HTML handoff artifact. It does not add PDF generation, a report designer, new endpoints, live provider fetching or AI/model behavior.

Next recommended pass:

• Continue with a full-workspace browser smoke and then refine any remaining dense panels found during populated-state review.

Sprint 66 Live workspace smoke polish (2026-06-19)

Changed:

• Ran a live browser smoke against http://192.168.10.150:1202 across Overview, Data, Map, QA/QC, AI Labs, Exports and System.
• Captured desktop and mobile screenshots under artifacts/sprint66-live-workspace-smoke/.
• Confirmed all primary workspaces load with no console warnings/errors and no page-level horizontal overflow.
• Tightened Export Center handoff card CSS so action cards and readiness cells wrap by available width instead of forcing cramped three/four-column layouts in the populated Exports workspace.
• Added backend/tests/test_sprint66_live_workspace_smoke_polish.py.

Tested:

• Red step: python -m pytest backend/tests/test_sprint66_live_workspace_smoke_polish.py -q failed on missing width-aware export grid CSS and Sprint 66 log entry.
• Live desktop browser smoke: all seven primary workspaces opened, screenshots captured, no console warnings/errors, no horizontal page overflow.
• Live mobile browser smoke: all seven primary workspaces opened, screenshots captured, no console warnings/errors, no page-level horizontal overflow. The mobile primary nav intentionally remains a horizontal rail.
• Local post-fix browser check against http://127.0.0.1:5175 with live API proxy: Exports handoff action cards wrap to 2 desktop columns and 1 mobile column, no console warnings/errors and no horizontal page overflow.
• bash scripts/run_readiness_check.sh (237 passed)
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1 rebuilt and redeployed the Tower all-in-one container, with live migration smoke and browser runtime verification passing.
• Live post-deploy Exports check on http://192.168.10.150:1202: handoff action cards render as 2 usable columns at desktop width, no console warnings/errors and no horizontal overflow.

Open:

• None for this pass.

Limitations:

• This is a visual ergonomics hardening pass only. It does not add endpoints, migrations, provider fetching, AI/model behavior or new product capabilities.

Next recommended pass:

• Continue with populated Map/Data interaction polish, especially making it easier to activate the demo vector layer from the map empty state.

Sprint 67 Map empty-state quick actions (2026-06-19)

Changed:

• Added ready vector/GeoJSON dataset quick actions to the Map workspace empty state.
• Reused the existing openDatasetInMap frontend flow so quick actions load the same persisted dataset layer as the Data workspace button.
• Added width-aware .map-empty-action-grid styling for desktop and mobile.
• Added backend/tests/test_sprint67_map_empty_state_quick_actions.py.
• Updated frontend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Red step: python -m pytest backend/tests/test_sprint67_map_empty_state_quick_actions.py -q failed on missing Map quick-action props, App wiring and CSS.
• python -m pytest backend/tests/test_sprint67_map_empty_state_quick_actions.py backend/tests/test_sprint63_map_overlay_ergonomics.py backend/tests/test_sprint30_workbench_components.py -q (9 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• Local Chrome/Playwright check against http://127.0.0.1:5175 with live API proxy: Map empty state showed 2 ready dataset actions, clicking the first loaded demo_predicted_buildings.geojson as a 2-feature active layer, no console warnings/errors and no horizontal overflow.
• bash scripts/run_readiness_check.sh (240 passed)
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1 rebuilt and redeployed the Tower all-in-one container, with live migration smoke and browser runtime verification passing.
• Live post-deploy Map quick-action check on http://192.168.10.150:1202: Map empty state showed 2 ready dataset actions, clicking the first loaded demo_predicted_buildings.geojson as a 2-feature active layer, no console warnings/errors and no horizontal overflow.

Open:

• None for this pass.

Limitations:

• This is a frontend interaction polish pass only. It does not add map layers, backend endpoints, migrations, provider fetching or geospatial processing.

Next recommended pass:

• Continue with Data catalog density polish, especially making selected/reference/candidate dataset roles easier to scan in populated demo projects.

Sprint 68 Data catalog density polish (2026-06-19)

Changed:

• Added a compact role summary to the Data catalog for Selected, Reference, Candidate and Source datasets.
• Added scan-friendly dataset card badges plus source, reference layer and CRS context.
• Treated non-reference vector/GeoJSON datasets as QA Candidate display roles in the frontend only, preserving persisted dataset_role values and API contracts.
• Added responsive role-summary and role-badge CSS.
• Added backend/tests/test_sprint68_dataset_catalog_density.py.
• Updated frontend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Red step: python -m pytest backend/tests/test_sprint68_dataset_catalog_density.py -q failed on missing role summary, role badges and responsive CSS.
• python -m pytest backend/tests/test_sprint68_dataset_catalog_density.py backend/tests/test_sprint67_map_empty_state_quick_actions.py backend/tests/test_sprint30_workbench_components.py -q (10 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• Local browser check against http://127.0.0.1:5175 with live API proxy: Data workspace showed Selected, Reference, Candidate and Source summary cards, with 1 Reference, 1 Candidate and 0 Source in the demo catalog; dataset badges rendered correctly on desktop and mobile, no console warnings/errors and no horizontal overflow.
• Screenshots captured under artifacts/sprint68-dataset-catalog-density/.
• bash scripts/run_readiness_check.sh (243 passed)
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1 rebuilt and redeployed the Tower all-in-one container, with live migration smoke and browser runtime verification passing.
• Live post-deploy Data catalog check on http://192.168.10.150:1202: Data workspace showed Selected, Reference, Candidate and Source summary cards, candidate/reference badges rendered, no console warnings/errors and no horizontal overflow on desktop or mobile.

Open:

• None for this pass.

Limitations:

• This is a frontend density/readability pass only. It does not change dataset persistence, backend role validation, API contracts, migrations, provider fetching or AI/model behavior.

Next recommended pass:

• Continue with Data workspace action polish, especially making QA/export affordances clearer once candidate and reference layers are present.

Sprint 69 Data catalog action polish (2026-06-19)

Changed:

• Added role-aware recommended action hints to dataset cards.
• Reworked dataset card actions into compact two-line buttons for Inspect, Map, Export / QA and Metadata.
• Kept all existing Data workspace handlers and API calls unchanged.
• Added disabled-action explanation copy for unsupported export/QA and raster metadata refresh cases.
• Added responsive .dataset-action-grid and .dataset-action-button CSS.
• Added backend/tests/test_sprint69_dataset_action_polish.py.
• Updated frontend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Red step: python -m pytest backend/tests/test_sprint69_dataset_action_polish.py -q failed on missing action hints, action grid and responsive styles.
• python -m pytest backend/tests/test_sprint69_dataset_action_polish.py backend/tests/test_sprint68_dataset_catalog_density.py backend/tests/test_sprint29_dataset_components.py -q (9 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• Local browser check against http://127.0.0.1:5175 with live API proxy: Data workspace rendered 2 role-aware hints and 8 compact dataset actions across the demo candidate/reference datasets, no console warnings/errors and no horizontal overflow on desktop or mobile.
• Screenshots captured under artifacts/sprint69-dataset-action-polish/.
• bash scripts/run_readiness_check.sh (246 passed)
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1 rebuilt and redeployed the Tower all-in-one container, with live migration smoke and browser runtime verification passing.
• Live post-deploy Data action check on http://192.168.10.150:1202: Data workspace rendered 2 role-aware hints and 8 compact dataset actions across the demo candidate/reference datasets, no console warnings/errors and no horizontal overflow on desktop or mobile.

Open:

• None for this pass.

Limitations:

• This is a frontend affordance/readability pass only. It does not add new workflows, change dataset persistence, change API contracts, add migrations, fetch providers or alter AI/model behavior.

Next recommended pass:

• Continue with QA/QC workspace handoff polish, especially linking the existing candidate/reference dataset context more clearly to persisted QA results.

Sprint 70 QA/QC handoff polish (2026-06-19)

Changed:

• Added candidate/reference handoff cards to the QA/QC results panel.
• Resolved persisted quality-check candidate/reference dataset IDs to loaded dataset names when available.
• Passed candidateDatasets and referenceDatasets from App.tsx into QualityResultsPanel.
• Tightened QA candidate context to non-reference vector/GeoJSON datasets while preserving persisted dataset_role values.
• Added responsive .quality-handoff-grid, .quality-dataset-name and .quality-check-dataset-link CSS.
• Added backend/tests/test_sprint70_quality_handoff_polish.py.
• Updated frontend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Red step: python -m pytest backend/tests/test_sprint70_quality_handoff_polish.py -q failed on missing dataset context props, handoff markup, App wiring and styles.
• python -m pytest backend/tests/test_sprint70_quality_handoff_polish.py backend/tests/test_sprint39_frontend_orchestration_hooks.py backend/tests/test_sprint51_quality_export_polish.py -q (15 passed)
• python -m pytest backend/tests/test_sprint70_quality_handoff_polish.py backend/tests/test_sprint51_quality_export_polish.py backend/tests/test_sprint30_workbench_components.py backend/tests/test_sprint27_frontend_workflow_hooks.py -q (14 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• Local browser check against http://127.0.0.1:5175 with live API proxy: QA/QC workspace showed 1 candidate layer, 1 reference layer, latest comparison names and named dataset links inside persisted quality-check cards, with no console warnings/errors and no horizontal overflow.
• Screenshots captured under artifacts/sprint70-quality-handoff-polish/.
• bash scripts/run_readiness_check.sh (250 passed)
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1 rebuilt and redeployed the Tower all-in-one container, with live migration smoke and browser runtime verification passing.
• Live post-deploy QA/QC handoff check on http://192.168.10.150:1202: QA/QC workspace showed 1 candidate layer, 1 reference layer, latest comparison names and named dataset links inside persisted quality-check cards, with no console warnings/errors and no horizontal overflow on desktop or mobile.

Open:

• None for this pass.

Limitations:

• This is a frontend handoff/readability pass only. It does not change QA persistence, metric calculations, API contracts, migrations, provider fetching or AI/model behavior.

Next recommended pass:

• Continue with QA/QC result card polish, especially making metric groups easier to scan in long-lived demo projects.

Sprint 71 QA/QC metric card polish (2026-06-19)

Changed:

• Added core metric evidence cards to QA/QC result cards for precision, recall, F1, mean IoU, false positives and false negatives.
• Kept the raw persisted metric list visible below the promoted evidence cards.
• Added compact metric label/value formatting in QualityResultsPanel.
• Added responsive .quality-metric-grid, .quality-metric-card and .quality-metric-card-critical CSS.
• Added backend/tests/test_sprint71_quality_metric_polish.py.
• Updated frontend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Red step: python -m pytest backend/tests/test_sprint71_quality_metric_polish.py -q failed on missing metric promotion helpers, markup and styles.
• python -m pytest backend/tests/test_sprint71_quality_metric_polish.py backend/tests/test_sprint70_quality_handoff_polish.py backend/tests/test_sprint51_quality_export_polish.py backend/tests/test_sprint30_workbench_components.py -q (13 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• Local browser check against http://127.0.0.1:5175 with live API proxy: QA/QC workspace rendered 12 promoted metric cards across 2 persisted quality checks, retained 2 raw metric sections, no console warnings/errors and no horizontal overflow on desktop or mobile.
• Screenshots captured under artifacts/sprint71-quality-metric-polish/.
• bash scripts/run_readiness_check.sh (253 passed)
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1 rebuilt and redeployed the Tower all-in-one container, with live migration smoke and browser runtime verification passing.
• Live post-deploy QA/QC metric check on http://192.168.10.150:1202: QA/QC workspace rendered 12 promoted metric cards across 2 persisted quality checks, retained 2 raw metric sections, no console warnings/errors and no body-level horizontal overflow on desktop or mobile.
• Live mobile screenshot captured at artifacts/sprint71-quality-metric-polish/live-qa-metrics-mobile.png.

Open:

• None for this pass.

Limitations:

• Frontend readability pass only; no QA calculation, persistence, API, migrations, provider or AI/model changes.

Next recommended pass:

• Continue with QA/QC metric/result filtering or result-card density for long-lived demo projects.

Sprint 72 Mobile overflow hardening (2026-06-20)

Changed:

• Clamped page-level horizontal overflow for html, body and the workbench shell.
• Kept mobile sidebar navigation and workspace shortcut chips as contained horizontal scroll regions with overscroll containment.
• Added min-width/max-width containment for the workbench layout, main area, inspector panel and card surfaces.
• Allowed long QA check IDs, dataset links and inspector values to wrap instead of widening result cards.
• Switched inspector tabs to a two-column layout on narrow screens.
• Added backend/tests/test_sprint72_mobile_overflow_hardening.py.
• Updated frontend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Red step: python -m pytest backend/tests/test_sprint72_mobile_overflow_hardening.py -q failed on missing mobile overflow and identifier wrapping contracts.
• python -m pytest backend/tests/test_sprint72_mobile_overflow_hardening.py backend/tests/test_sprint71_quality_metric_polish.py backend/tests/test_sprint62_frontend_visual_polish.py backend/tests/test_sprint30_workbench_components.py -q (11 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• Local browser check against http://127.0.0.1:5176 with live API proxy at a 390px viewport: QA/QC workspace rendered 2 quality cards and 12 metric cards; body, shell, inspector, inspector panel and inspector tabs stayed within viewport width; sidebar/workspace nav retained contained horizontal scrolling; no console warnings/errors.
• Screenshot captured under artifacts/sprint72-mobile-overflow-hardening/.
• bash scripts/run_readiness_check.sh (255 passed)
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1 rebuilt and redeployed the Tower all-in-one container, with live migration smoke and browser runtime verification passing.
• Live post-deploy mobile check on http://192.168.10.150:1202 at a 390px viewport: QA/QC workspace rendered 2 quality cards and 12 metric cards; body, shell, inspector, inspector panel and inspector tabs stayed within viewport width; sidebar/workspace nav retained contained horizontal scrolling; no console warnings/errors.
• Live screenshot captured at artifacts/sprint72-mobile-overflow-hardening/live-mobile-qa.png.

Open:

• None for this pass.

Limitations:

• Frontend CSS hardening only; no UI flow, API, persistence, migration, provider or AI/model changes.

Next recommended pass:

• Continue with QA/QC result filtering/density for long-lived demo projects, or a broader mobile visual pass across Data and Map once the overflow baseline is stable.

Sprint 73 QA/QC result filtering (2026-06-20)

Changed:

• Added client-side QA/QC result search across check id, type, status, candidate/reference dataset ids, analysis/job ids and resolved dataset names.
• Added status and check-type filters derived from the loaded quality-check list.
• Added latest-eight result density control with a show-all toggle.
• Added a no-match empty state and reset action for filtered result views.
• Reused the dense history control styling pattern while allowing the QA/QC controls to wrap inside narrower workspace columns.
• Added backend/tests/test_sprint73_quality_result_filtering.py.
• Updated frontend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Red step: python -m pytest backend/tests/test_sprint73_quality_result_filtering.py -q failed on missing filter state, filtered list logic and styles.
• python -m pytest backend/tests/test_sprint73_quality_result_filtering.py backend/tests/test_sprint72_mobile_overflow_hardening.py backend/tests/test_sprint71_quality_metric_polish.py backend/tests/test_sprint70_quality_handoff_polish.py backend/tests/test_sprint51_quality_export_polish.py -q (13 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• Local browser check against http://127.0.0.1:5177 with live API proxy: QA/QC filters rendered against 2 persisted demo checks; search/status conflict showed the no-match state; reset restored 2 checks and 12 metric cards; no console warnings/errors and no body-level overflow.
• Local mobile browser check at a 390px viewport: filter controls stayed within viewport width, with 2 checks and 12 metric cards visible.
• Screenshots captured under artifacts/sprint73-quality-result-filtering/.
• bash scripts/run_readiness_check.sh (257 passed)
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1 rebuilt and redeployed the Tower all-in-one container, with live migration smoke and browser runtime verification passing.
• Live post-deploy QA/QC filtering check on http://192.168.10.150:1202: search/status conflict showed the no-match state; reset restored 2 checks and 12 metric cards; no console warnings/errors and no body-level overflow.
• Live mobile check at a 390px viewport: QA/QC filter controls stayed within viewport width, with 2 checks and 12 metric cards visible.
• Live screenshots captured at artifacts/sprint73-quality-result-filtering/live-qa-filtering.png and artifacts/sprint73-quality-result-filtering/live-qa-filtering-mobile.png.

Open:

• None for this pass.

Limitations:

• Frontend filtering/density pass only; filtering is client-side over already-loaded persisted checks and does not change API pagination, persistence, QA calculations, migrations, provider behavior or AI/model behavior.

Next recommended pass:

• Continue with Data/Map mobile visual polish, especially dataset upload/action forms and map toolbar density on narrow screens.

Sprint 74 Data/Map mobile visual polish (2026-06-20)

Changed:

• Added mobile-density CSS for Data workspace upload forms, file input sizing and dataset action grids.
• Kept the desktop dataset action grid width contract intact while adding compact mobile tracks.
• Added touch/width containment for map toolbar controls, range sliders and empty-map quick actions.
• Reduced mobile map container height to keep controls and map visible together on narrow screens.
• Added backend/tests/test_sprint74_data_map_mobile_polish.py.
• Updated frontend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Red step: python -m pytest backend/tests/test_sprint74_data_map_mobile_polish.py -q failed on missing mobile Data/Map CSS contracts.
• python -m pytest backend/tests/test_sprint74_data_map_mobile_polish.py backend/tests/test_sprint72_mobile_overflow_hardening.py backend/tests/test_sprint69_dataset_action_polish.py backend/tests/test_sprint68_dataset_catalog_density.py backend/tests/test_sprint67_map_empty_state_quick_actions.py backend/tests/test_sprint63_map_overlay_ergonomics.py backend/tests/test_sprint50_workspace_usability_polish.py backend/tests/test_sprint30_workbench_components.py -q (21 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• Local browser check against http://127.0.0.1:5178 with live API proxy at a 390px viewport: Data upload form, file input and 8 dataset action buttons stayed within the viewport with no body-level horizontal overflow; Map toolbar, 2 layer controls, range sliders and empty-map quick actions also stayed within the viewport.
• Screenshots captured under artifacts/sprint74-data-map-mobile-polish/.
• bash scripts/run_readiness_check.sh (259 passed)
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1 rebuilt and redeployed the Tower all-in-one container on port 1202, with live migration smoke and browser runtime verification passing.
• Live post-deploy browser check on http://192.168.10.150:1202 at a 390px viewport: Data upload form, file input and 8 dataset action buttons stayed within the viewport; Map toolbar, 2 layer controls, range sliders and empty-map quick actions stayed within the viewport; no body-level horizontal overflow was detected.
• Live screenshots captured at artifacts/sprint74-data-map-mobile-polish/live-data-mobile.png and artifacts/sprint74-data-map-mobile-polish/live-map-mobile.png.

Open:

• None for this pass.

Limitations:

• Frontend CSS polish only; no Data/Map workflow, API, persistence, migration, provider or AI/model changes.

Next recommended pass:

• Continue with AI Labs mobile/density polish, especially detection and segmentation forms/tables.

Sprint 75 AI Labs mobile visual polish (2026-06-20)

Changed:

• Added mobile-density CSS for Detection and Segmentation Lab model cards, run forms and result summaries.
• Added overflow wrapping for long model ids, source tile paths, mask paths and QA summary values.
• Kept AI result tables scroll-contained while reducing their mobile minimum width.
• Added backend/tests/test_sprint75_ai_labs_mobile_polish.py.
• Updated frontend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Red step: python -m pytest backend/tests/test_sprint75_ai_labs_mobile_polish.py -q failed on missing AI Labs mobile CSS contracts.
• python -m pytest backend/tests/test_sprint75_ai_labs_mobile_polish.py backend/tests/test_sprint74_data_map_mobile_polish.py backend/tests/test_sprint72_mobile_overflow_hardening.py backend/tests/test_sprint62_frontend_visual_polish.py backend/tests/test_sprint30_workbench_components.py backend/tests/test_sprint8c_detection_visualization_qa.py backend/tests/test_sprint9_segmentation_foundation.py -q (30 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• Local browser check against http://127.0.0.1:5179 with live API proxy at a 390px viewport: AI Labs rendered 2 panels, 7 model cards, 6 lab blocks and 4 lab forms with no body-level horizontal overflow.
• Screenshot captured under artifacts/sprint75-ai-labs-mobile-polish/.
• bash scripts/run_readiness_check.sh (261 passed)
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1 rebuilt and redeployed the Tower all-in-one container on port 1202, with live migration smoke and browser runtime verification passing.
• Live post-deploy browser check on http://192.168.10.150:1202 at a 390px viewport: AI Labs rendered 2 panels, 7 model cards, 6 lab blocks and 4 lab forms with no body-level horizontal overflow.
• Live screenshot captured at artifacts/sprint75-ai-labs-mobile-polish/live-ai-labs-mobile.png.

Open:

• None for this pass.

Limitations:

• Frontend CSS polish only; no Detection/Segmentation workflow, API, persistence, migration, provider or AI/model changes.

Next recommended pass:

• Continue with Export/System mobile-density polish, especially export action cards and provider capability lists.

Sprint 76 Export/System mobile visual polish (2026-06-20)

Changed:

• Added scan-friendly Provider Capabilities cards with structured provider header, status badge, authority/configuration/geometry/query mode fields and layer chips.
• Added mobile-density CSS for provider cards, export action cards, export history controls and export card headers.
• Added overflow wrapping for long provider limitations, attribution/license notes, export ids and artifact paths.
• Added backend/tests/test_sprint76_export_system_mobile_polish.py.
• Updated frontend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Red step: python -m pytest backend/tests/test_sprint76_export_system_mobile_polish.py -q failed on missing Export/System mobile CSS and provider markup contracts.
• python -m pytest backend/tests/test_sprint76_export_system_mobile_polish.py backend/tests/test_sprint75_ai_labs_mobile_polish.py backend/tests/test_sprint74_data_map_mobile_polish.py backend/tests/test_sprint31_unraid_template.py backend/tests/test_sprint64_export_handoff_polish.py backend/tests/test_sprint7b_provider_registry.py backend/tests/test_sprint30_workbench_components.py -q (27 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh (263 passed)
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1 rebuilt and redeployed the Tower all-in-one container on port 1202, with live migration smoke and browser runtime verification passing.

Open:

• None for this pass.

Limitations:

• Frontend presentation polish only; no export workflow, provider API, persistence, migration, provider fetching or AI/model changes.

Next recommended pass:

• Continue with right-side inspector mobile polish, especially long dataset/vector/raster metadata and action groups.

Sprint 77 Inspector mobile visual polish (2026-06-20)

Changed:

• Added compact inspector action button grids and mobile-safe wrapping for long inspector values.
• Added scroll/wrap containment for dataset detail text, job JSON and loaded feature metadata.
• Added dataset-tool-panel and dataset-tool-group classes to raster/vector operation controls.
• Added CSS containment for raster/vector tool labels, inputs, selects and buttons inside the inspector.
• Added backend/tests/test_sprint77_inspector_mobile_polish.py.
• Updated frontend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Red step: python -m pytest backend/tests/test_sprint77_inspector_mobile_polish.py -q failed on missing inspector mobile CSS and dataset tool markup contracts.
• python -m pytest backend/tests/test_sprint77_inspector_mobile_polish.py backend/tests/test_sprint76_export_system_mobile_polish.py backend/tests/test_sprint75_ai_labs_mobile_polish.py backend/tests/test_sprint72_mobile_overflow_hardening.py backend/tests/test_sprint52_workbench_inspector_tabs.py backend/tests/test_sprint30_workbench_components.py backend/tests/test_sprint29_dataset_components.py -q (18 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh (265 passed)
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1 rebuilt and redeployed the Tower all-in-one container on port 1202, with live migration smoke and browser runtime verification passing.

Open:

• None for this pass.

Limitations:

• Frontend presentation polish only; no dataset workflow, raster/vector operation behavior, API, persistence, migration, provider fetching or AI/model changes.

Next recommended pass:

• Continue with report/export preview readability polish, especially large JSON/HTML handoff artifacts.

Sprint 78 Export preview readability polish (2026-06-20)

Changed:

• Added preview summary cards for JSON/GeoJSON export payload root type, root key count and rendered preview size.
• Added a scroll-contained export preview JSON shell and toolbar around the existing stored payload preview.
• Added wrapping for long JSON keys/values inside the preview body.
• Added backend/tests/test_sprint78_export_preview_readability.py.
• Updated frontend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Red step: python -m pytest backend/tests/test_sprint78_export_preview_readability.py -q failed on missing preview summary/shell CSS and markup contracts.
• python -m pytest backend/tests/test_sprint78_export_preview_readability.py backend/tests/test_sprint77_inspector_mobile_polish.py backend/tests/test_sprint76_export_system_mobile_polish.py backend/tests/test_sprint51_quality_export_polish.py backend/tests/test_sprint64_export_handoff_polish.py backend/tests/test_sprint17_export_foundation.py -q (20 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh (267 passed; frontend typecheck/build passed; Alembic single head 202606120900)
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1 rebuilt and redeployed the Tower all-in-one container on port 1202, then passed live migration smoke and browser runtime verification.

Open:

• None for this pass.

Limitations:

• Frontend presentation polish only; no export content, export API, artifact storage, migration, provider fetching or AI/model changes.

Next recommended pass:

• Continue with accessibility/keyboard focus polish across primary workbench controls.

Sprint 79 Accessibility focus polish (2026-06-20)

Changed:

• Added a shared visible keyboard focus contract for primary buttons, workspace navigation, command chips, inspector tabs and dataset action buttons.
• Added explicit ARIA labels for workspace sidebar navigation, workspace command chips and overview quick actions.
• Bound inspector tabs to active tab panels with aria-controls, tab ids, tabpanel role and aria-labelledby.
• Added backend/tests/test_sprint79_accessibility_focus_polish.py.
• Updated frontend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Red step: python -m pytest backend/tests/test_sprint79_accessibility_focus_polish.py -q failed on missing focus-visible CSS, navigation labels and inspector tab/panel bindings.
• python -m pytest backend/tests/test_sprint79_accessibility_focus_polish.py backend/tests/test_sprint77_inspector_mobile_polish.py backend/tests/test_sprint53_selection_ergonomics.py backend/tests/test_sprint49_workbench_shell_refactor.py backend/tests/test_sprint47_workbench_interaction_smoke.py -q (15 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh (270 passed; frontend typecheck/build passed; Alembic single head 202606120900)
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1 rebuilt and redeployed the Tower all-in-one container on port 1202, then passed live migration smoke and browser runtime verification.

Open:

• None for this pass.

Limitations:

• Frontend accessibility/presentation polish only; no workflow behavior, API contract, persistence, migration, provider fetching or AI/model changes.

Next recommended pass:

• Continue with form-level validation/readability polish for dense raster/vector operation panels.

Sprint 80 Operation form readability polish (2026-06-20)

Changed:

• Added structured headings, helper text, field wrappers, responsive field grids and action rows to raster operation controls.
• Added equivalent readability structure to vector clip, buffer and intersect controls.
• Added inline dataset tool error styling for raster clip/tile validation messages.
• Added backend/tests/test_sprint80_operation_form_readability.py.
• Updated frontend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Red step: python -m pytest backend/tests/test_sprint80_operation_form_readability.py -q failed on missing form readability CSS and markup contracts.
• python -m pytest backend/tests/test_sprint80_operation_form_readability.py -q (3 passed)
• python -m pytest backend/tests/test_sprint80_operation_form_readability.py backend/tests/test_sprint77_inspector_mobile_polish.py backend/tests/test_sprint29_dataset_components.py backend/tests/test_sprint28_dataset_workflow_hook.py backend/tests/test_vector_operations_service.py backend/tests/test_raster_operations_service.py -q (37 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh (273 passed; frontend typecheck/build passed; Alembic single head 202606120900)
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1 rebuilt and redeployed the Tower all-in-one container on port 1202, then passed live migration smoke and browser runtime verification.

Open:

• None for this pass.

Limitations:

• Frontend readability/presentation polish only; no raster/vector operation behavior, API contract, persistence, migration, provider fetching or AI/model changes.

Next recommended pass:

• Continue with compact empty/error-state polish across QA, exports and AI lab result panels.

Sprint 81 Result state consistency polish (2026-06-20)

Changed:

• Added shared result-state CSS variants for loading, error, empty and ready states.
• Applied result-state blocks to QA/QC error/empty/filter-empty states.
• Applied result-state blocks to export loading, error and empty/filter-empty states.
• Applied result-state blocks to Detection and Segmentation model loading/errors, empty registries, result counts and QA/run errors.
• Added backend/tests/test_sprint81_result_state_polish.py.
• Updated frontend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Red step: python -m pytest backend/tests/test_sprint81_result_state_polish.py -q failed on missing result-state CSS and panel markup contracts.
• python -m pytest backend/tests/test_sprint81_result_state_polish.py -q (3 passed)
• python -m pytest backend/tests/test_sprint81_result_state_polish.py backend/tests/test_sprint80_operation_form_readability.py backend/tests/test_sprint75_ai_labs_mobile_polish.py backend/tests/test_sprint76_export_system_mobile_polish.py backend/tests/test_sprint73_quality_result_filtering.py backend/tests/test_sprint70_quality_handoff_polish.py backend/tests/test_sprint8c_detection_visualization_qa.py backend/tests/test_sprint9_segmentation_foundation.py -q (34 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh (276 passed; frontend typecheck/build passed; Alembic single head 202606120900)
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1 rebuilt and redeployed the Tower all-in-one container on port 1202, then passed live migration smoke and browser runtime verification.

Open:

• None for this pass.

Limitations:

• Frontend presentation/state polish only; no workflow behavior, API contract, persistence, migration, provider fetching or AI/model changes.

Next recommended pass:

• Continue with visual density review for topbar/sidebar/responsive shell after another live browser pass.

Sprint 82 Shell density polish (2026-06-20)

Changed:

• Added a keyboard skip link that targets the active workspace main region.
• Added an explicit Primary workspaces label to the sidebar navigation and a focusable workspace-main target.
• Tightened narrow-screen topbar, context chip, sidebar nav and workspace shortcut density while keeping intentional horizontal rails scroll-safe.
• Added backend/tests/test_sprint82_shell_density_polish.py.
• Updated frontend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Live browser pre-check against http://192.168.10.150:1202 showed no console errors, no horizontal document overflow and a too-tall narrow viewport topbar/context stack.
• Red step: python -m pytest backend/tests/test_sprint82_shell_density_polish.py -q failed on missing skip-link, main focus target and compact mobile shell CSS contracts.
• python -m pytest backend/tests/test_sprint82_shell_density_polish.py -q (3 passed)
• python -m pytest backend/tests/test_sprint82_shell_density_polish.py backend/tests/test_sprint79_accessibility_focus_polish.py backend/tests/test_sprint72_mobile_overflow_hardening.py backend/tests/test_sprint49_workbench_shell_refactor.py backend/tests/test_sprint47_workbench_interaction_smoke.py -q (13 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh (279 passed; frontend typecheck/build passed; Alembic single head 202606120900)
• Live post-deploy browser check found a narrow-screen cascade override that made .context-bar one-column again; tightened the regression test and kept the context rail scrollable through the smallest breakpoint.
• Re-ran python -m pytest backend/tests/test_sprint82_shell_density_polish.py -q, cd frontend && npm run typecheck, cd frontend && npm run build and bash scripts/run_readiness_check.sh (279 passed) after that fix.
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1 rebuilt and redeployed the Tower all-in-one container on port 1202, then passed live migration smoke and browser runtime verification.
• Final live browser check against http://192.168.10.150:1202 passed on desktop and mobile viewports: no console warnings/errors, no horizontal document overflow, skip link/main target present, Primary workspaces nav label present and mobile context bar stayed compact at 64px tall.

Open:

• None for this pass.

Limitations:

• Frontend shell presentation/accessibility polish only; no workflow behavior, API contract, persistence, migration, provider fetching or AI/model changes.

Next recommended pass:

• Continue with a live visual review of dense workspace panel hierarchy after the shell density changes are deployed.

Sprint 83 Workspace panel hierarchy polish (2026-06-20)

Changed:

• Made the Overview readiness strip visually calmer with a compact section surface and tighter status tiles.
• Added explicit overview-action-copy, overview-quick-actions and quick-action-button regions to the recommended-action block.
• Restyled the recommended flow block as a lighter accent callout so it no longer competes visually with the readiness strip.
• Added backend/tests/test_sprint83_workspace_panel_hierarchy.py.
• Updated frontend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Live browser pre-check against http://192.168.10.150:1202 showed no console errors or horizontal overflow, but the first workspace viewport still had several equally weighted white cards.
• Red step: python -m pytest backend/tests/test_sprint83_workspace_panel_hierarchy.py -q failed on missing Overview hierarchy regions and compact section-surface CSS contracts.
• python -m pytest backend/tests/test_sprint83_workspace_panel_hierarchy.py -q (3 passed)
• python -m pytest backend/tests/test_sprint83_workspace_panel_hierarchy.py backend/tests/test_sprint82_shell_density_polish.py backend/tests/test_sprint62_frontend_visual_polish.py backend/tests/test_sprint50_workspace_usability_polish.py backend/tests/test_sprint22_workbench_status_strip.py backend/tests/test_sprint47_workbench_interaction_smoke.py -q (15 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh (282 passed; frontend typecheck/build passed; Alembic single head 202606120900)
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1 rebuilt and redeployed the Tower all-in-one container on port 1202, then passed live migration smoke and browser runtime verification.
• Final live browser check against http://192.168.10.150:1202 passed on desktop and mobile viewports: no console warnings/errors, no horizontal document overflow, Overview hierarchy regions present and four recommended-action buttons rendered.

Open:

• None for this pass.

Limitations:

• Frontend Overview presentation hierarchy only; no workflow behavior, API contract, persistence, migration, provider fetching or AI/model changes.

Next recommended pass:

• Continue with Data workspace panel hierarchy and selected-item density after this pass is deployed and visually checked.

Sprint 84 Data workspace density polish (2026-06-20)

Changed:

• Added selected-summary regions to Project, AOI and Dataset panels so the active data context appears before creation/upload forms.
• Split Project and AOI panels into data-panel-form-block and data-panel-list-block regions.
• Split Dataset catalog into selected dataset, upload source data and available dataset regions.
• Restyled dataset upload as an embedded source-data block while preserving the existing upload flow.
• Added backend/tests/test_sprint84_data_workspace_density.py.
• Updated frontend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Live browser pre-check against http://192.168.10.150:1202 showed Data workspace had no console errors or horizontal overflow, but the first Data viewport was still form-first and visually heavy.
• Red step: python -m pytest backend/tests/test_sprint84_data_workspace_density.py -q failed on missing selected-summary and named form/list/catalog regions.
• python -m pytest backend/tests/test_sprint84_data_workspace_density.py -q (3 passed)
• python -m pytest backend/tests/test_sprint84_data_workspace_density.py backend/tests/test_sprint83_workspace_panel_hierarchy.py backend/tests/test_sprint50_workspace_usability_polish.py backend/tests/test_sprint29_dataset_components.py backend/tests/test_sprint68_dataset_catalog_density.py backend/tests/test_sprint69_dataset_action_polish.py backend/tests/test_sprint47_workbench_interaction_smoke.py -q (20 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh (285 passed; frontend typecheck/build passed; Alembic single head 202606120900)
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1 rebuilt and redeployed the Tower all-in-one container on port 1202, then passed live migration smoke and browser runtime verification.
• Final live browser DOM/console check against http://192.168.10.150:1202 passed on desktop and mobile viewports: Data workspace active, no console warnings/errors, no horizontal document overflow, three selected-summary regions, three form blocks and three list/catalog blocks rendered.

Open:

• None for this pass.

Limitations:

• Frontend Data workspace presentation hierarchy only; no upload behavior, API contract, persistence, migration, provider fetching or AI/model changes.

Next recommended pass:

• Continue with Map workspace panel hierarchy and layer-control density after this pass is deployed and visually checked.

Sprint 85 Map workspace density polish (2026-06-20)

Changed:

• Added a compact Map workspace context summary for selected AOI, active layer and rendered feature state.
• Wrapped existing layer controls and provenance in a dedicated map-control-surface.
• Wrapped the existing MapLibre component in a map-frame-surface and the selected-feature inspector in a map-inspection-surface.
• Added backend/tests/test_sprint85_map_workspace_density.py.
• Updated frontend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Live browser pre-check against http://192.168.10.150:1202 showed Map workspace had no console errors or horizontal overflow, but controls, provenance, map frame and inspector still read as a loose vertical stack.
• Red step: python -m pytest backend/tests/test_sprint85_map_workspace_density.py -q failed on missing Map workspace surface and context-summary contracts.
• python -m pytest backend/tests/test_sprint85_map_workspace_density.py -q (3 passed)
• python -m pytest backend/tests/test_sprint85_map_workspace_density.py backend/tests/test_sprint63_map_overlay_ergonomics.py backend/tests/test_sprint67_map_empty_state_quick_actions.py backend/tests/test_sprint74_data_map_mobile_polish.py backend/tests/test_sprint47_workbench_interaction_smoke.py -q (13 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• Live browser post-deploy check showed desktop Map workspace was structured and overflow-free, but the mobile breakpoint still stacked controls too tall above the map.
• Red step: python -m pytest backend/tests/test_sprint85_map_workspace_density.py -q then failed on missing compact mobile Map workspace breakpoint contracts.
• python -m pytest backend/tests/test_sprint85_map_workspace_density.py -q (4 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh (289 passed; frontend typecheck/build passed; Alembic single head 202606120900)
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1 rebuilt and redeployed the Tower all-in-one container on port 1202, then passed live migration smoke and browser runtime verification.
• Final live browser check against http://192.168.10.150:1202 passed on desktop and mobile viewports: no console warnings/errors, no horizontal document overflow, three Map context summary cards, two layer control cards, separated control/map/inspector surfaces and mobile control preamble reduced after the compact breakpoint correction.

Open:

• None for this pass.

Limitations:

• Frontend Map workspace presentation hierarchy only; no overlay behavior, API contract, persistence, migration, provider fetching or AI/model changes.

Next recommended pass:

• Continue with QA/QC workspace result hierarchy and filter density after this pass is deployed and visually checked.

Sprint 86 QA/QC workspace density polish (2026-06-20)

Changed:

• Wrapped QA/QC summary metrics in quality-summary-surface.
• Wrapped candidate/reference/latest comparison context in quality-evidence-surface.
• Wrapped refresh, result states, filters and list limit controls in quality-control-surface.
• Wrapped persisted quality check cards in quality-history-surface.
• Added compact mobile CSS contracts for QA/QC summary, handoff, filter, score, metric and raw metric grids.
• Added backend/tests/test_sprint86_quality_workspace_density.py.
• Updated frontend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Live browser pre-check against http://192.168.10.150:1202 showed QA/QC workspace had no console errors or horizontal overflow, but summary, handoff, filters and history were still visually stacked at equal weight. Mobile measured summary at 210px, handoff at 295px and filters at 278px before the result history.
• Red step: python -m pytest backend/tests/test_sprint86_quality_workspace_density.py -q failed on missing QA/QC surface and density CSS contracts.
• python -m pytest backend/tests/test_sprint86_quality_workspace_density.py -q (3 passed)
• python -m pytest backend/tests/test_sprint86_quality_workspace_density.py backend/tests/test_sprint70_quality_handoff_polish.py backend/tests/test_sprint71_quality_metric_polish.py backend/tests/test_sprint73_quality_result_filtering.py backend/tests/test_sprint81_result_state_polish.py backend/tests/test_sprint47_workbench_interaction_smoke.py -q (18 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• First full readiness run caught a compatibility regression in backend/tests/test_sprint51_quality_export_polish.py: the root quality-results-panel class was no longer exact. Kept the old root class and moved the new shell into a wrapper.
• python -m pytest backend/tests/test_sprint51_quality_export_polish.py backend/tests/test_sprint86_quality_workspace_density.py backend/tests/test_sprint70_quality_handoff_polish.py backend/tests/test_sprint73_quality_result_filtering.py -q (11 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh (292 passed; frontend typecheck/build passed; Alembic single head 202606120900)
• powershell -NoProfile -ExecutionPolicy Bypass -File scripts/deploy_tower.ps1 rebuilt and redeployed the Tower all-in-one container on port 1202, then passed live migration smoke and browser runtime verification.
• Final live browser check against http://192.168.10.150:1202 passed on desktop and mobile viewports: no console warnings/errors, no horizontal document overflow, QA/QC shell/surfaces present and two persisted quality check cards rendered.

Open:

• None for this pass.

Limitations:

• Frontend QA/QC workspace presentation hierarchy only; no quality-check behavior, API contract, persistence, migration, provider fetching or AI/model changes.

Next recommended pass:

• Continue with Change Detection panel hierarchy and analysis workspace balance after this pass is deployed and visually checked.

Sprint 87 Change Detection density polish (2026-06-20)

Changed:

• Wrapped Change Detection input controls in change-detection-input-surface.
• Replaced loose error/empty text with shared result-state cards inside change-detection-state-stack.
• Wrapped result metrics in change-detection-result-surface.
• Wrapped warning output in change-detection-warning-surface.
• Added compact responsive CSS contracts for Change Detection form and summary grids.
• Added backend/tests/test_sprint87_change_detection_density.py.
• Updated frontend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Live browser pre-check attempt against http://192.168.10.150:1202 hit a transient browser automation click timeout on the QA/QC workspace nav; source review showed Change Detection was still the older header/form/summary stack.
• Red step: python -m pytest backend/tests/test_sprint87_change_detection_density.py -q failed on missing Change Detection surface and density CSS contracts.
• python -m pytest backend/tests/test_sprint87_change_detection_density.py backend/tests/test_sprint18_change_detection.py backend/tests/test_sprint39_frontend_orchestration_hooks.py backend/tests/test_sprint86_quality_workspace_density.py backend/tests/test_sprint47_workbench_interaction_smoke.py -q (20 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh (295 passed; frontend typecheck/build passed; Alembic head 202606120900; live smoke syntax passed)
• Tower deploy via scripts/deploy_tower.ps1 rebuilt and restarted the all-in-one container on http://192.168.10.150:1202.
• Tower deploy live migration smoke passed with PostGIS 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1 and required runtime schema objects present.
• Browser runtime verification passed for frontend, proxied API and icon.
• Direct Chrome live UI check passed on desktop 1440x1000 and mobile 390x844: Change Detection shell/input surface rendered, no horizontal overflow and no console warnings/errors.

Open:

• None known after full readiness, Tower deploy and live browser validation.

Limitations:

• Frontend Change Detection presentation hierarchy only; no change-detection behavior, API contract, persistence, migration, provider fetching or AI/model changes.

Next recommended pass:

• Continue with AI Labs run-form hierarchy and detection/segmentation result density after this pass is deployed and visually checked.

Sprint 88 AI Labs density polish (2026-06-20)

Changed:

• Wrapped Detection Lab and Segmentation Lab in shared ai-lab-shell workspace shells.
• Grouped model registry states into ai-lab-model-surface with a shared ai-lab-state-stack.
• Grouped run controls into ai-lab-run-surface.
• Grouped persisted detection/segmentation result loading and tables into ai-lab-results-surface.
• Grouped QA controls and metric summaries into ai-lab-qa-surface.
• Added shared AI Lab CSS contracts for compact model grids, form grids and summary grids.
• Added backend/tests/test_sprint88_ai_lab_density.py.
• Updated frontend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Red step: python -m pytest backend/tests/test_sprint88_ai_lab_density.py -q failed on missing AI Lab shells/surfaces and CSS contracts.
• python -m pytest backend/tests/test_sprint88_ai_lab_density.py backend/tests/test_sprint8c_detection_visualization_qa.py backend/tests/test_sprint9_segmentation_foundation.py backend/tests/test_sprint39_frontend_orchestration_hooks.py backend/tests/test_sprint47_workbench_interaction_smoke.py -q (34 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• Local browser UI check against http://127.0.0.1:5174 passed on desktop and mobile: Detection/Segmentation shells and model/run/result/QA surfaces rendered, no horizontal overflow and no console warnings/errors. The local-only screenshot showed expected API 500 states because only the frontend Vite server was running.
• First full readiness run caught legacy AI Lab static contract expectations for exact lab-block and result-summary-card class names. Kept those compatibility anchors while preserving the new AI Lab surfaces.
• python -m pytest backend/tests/test_sprint50_workspace_usability_polish.py backend/tests/test_sprint75_ai_labs_mobile_polish.py backend/tests/test_sprint88_ai_lab_density.py -q (8 passed)
• bash scripts/run_readiness_check.sh (299 passed; frontend typecheck/build passed; Alembic head 202606120900; live smoke syntax passed)
• Tower deploy via scripts/deploy_tower.ps1 rebuilt and restarted the all-in-one container on http://192.168.10.150:1202.
• Tower deploy live migration smoke passed with PostGIS 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1 and required runtime schema objects present.
• Browser runtime verification passed for frontend, proxied API and icon.
• Live browser AI Labs check against http://192.168.10.150:1202 passed on desktop and mobile: Detection/Segmentation shells and model/run/result/QA surfaces rendered, no horizontal overflow and no console warnings/errors.

Open:

• None known after full readiness, Tower deploy and live browser validation.

Limitations:

• Frontend AI Labs presentation hierarchy only; no detection/segmentation behavior, API contract, persistence, migration, provider fetching or AI/model changes.

Next recommended pass:

• Continue with export/system final visual consistency or live workflow guidance once this pass is deployed and visually checked.

Sprint 89 Export/System density polish (2026-06-20)

Changed:

• Wrapped Export Center content in export-center-shell.
• Grouped export summary, handoff readiness, artifact actions, current state cards and history into focused export-* surfaces.
• Wrapped Provider Capabilities content in system-provider-shell.
• Replaced loose provider loading/error/empty text with shared result-state cards.
• Grouped provider registry content into system-provider-capability-surface.
• Split provider limitation/reason text and attribution/license data into provider-detail-stack and provenance cards.
• Added shared Export/System density CSS for compact summary, handoff, action, history and provider provenance grids.
• Added backend/tests/test_sprint89_export_system_density.py.
• Updated frontend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Red step: python -m pytest backend/tests/test_sprint89_export_system_density.py -q failed on missing Export/System shells, surfaces and CSS contracts.
• python -m pytest backend/tests/test_sprint89_export_system_density.py backend/tests/test_sprint51_quality_export_polish.py backend/tests/test_sprint64_export_handoff_polish.py backend/tests/test_sprint76_export_system_mobile_polish.py backend/tests/test_sprint78_export_preview_readability.py -q (12 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• In-app browser fallback note: the Browser click path timed out on the local workspace navigation CDP command, so rendered validation used local Chrome via Playwright.
• Local Chrome UI check against http://127.0.0.1:5174 passed on desktop and mobile: Export/System shells and surfaces rendered with no horizontal overflow. Local console showed expected Vite proxy 500s because only the frontend server was running.
• bash scripts/run_readiness_check.sh (303 passed; frontend typecheck/build passed; Alembic head 202606120900; live smoke syntax passed)
• Tower deploy via scripts/deploy_tower.ps1 rebuilt and restarted the all-in-one container on http://192.168.10.150:1202.
• Tower deploy live migration smoke passed with PostGIS 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1 and required runtime schema objects present.
• Browser runtime verification passed for frontend, proxied API and icon.
• Live Chrome UI check against http://192.168.10.150:1202 passed on desktop and mobile: Export/System shells and surfaces rendered, four provider cards and eight provider provenance cards were visible, no horizontal overflow and no console warnings/errors.

Open:

• None known after full readiness, Tower deploy and live browser validation.

Limitations:

• Frontend Export/System presentation hierarchy only; no export behavior, API contract, persistence, migration, provider fetching or AI/model changes.

Next recommended pass:

• Continue with end-to-end workflow guidance after this pass is deployed and visually checked.

Sprint 90 workflow guidance polish (2026-06-20)

Changed:

• Added an Overview workflow-guidance-panel that shows the V1 flow from Project & AOI through Data, Map, QA / AI and Export.
• Added ready/waiting/next state labels from existing loaded project, dataset, map, QA/AI and export state.
• Routed the guidance cards through the existing setActiveWorkspace navigation only.
• Added compact responsive CSS for the guidance rail.
• Added backend/tests/test_sprint90_workflow_guidance.py.
• Updated frontend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Red step: python -m pytest backend/tests/test_sprint90_workflow_guidance.py -q failed on missing workflow guidance App and CSS contracts.
• python -m pytest backend/tests/test_sprint90_workflow_guidance.py backend/tests/test_sprint83_workspace_panel_hierarchy.py backend/tests/test_sprint49_workbench_shell_refactor.py backend/tests/test_sprint62_frontend_visual_polish.py backend/tests/test_sprint82_shell_density_polish.py -q (13 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh (306 passed; frontend typecheck/build passed; Alembic head 202606120900; live smoke syntax passed)
• Local browser UI check against http://127.0.0.1:5174 passed on desktop and mobile: workflow guidance panel rendered with five steps and no horizontal overflow. Local console showed expected Vite proxy 500s because only the frontend server was running.
• Tower deploy via scripts/deploy_tower.ps1 rebuilt and restarted the all-in-one container on http://192.168.10.150:1202 from commit 2a0e7e7.
• Tower deploy live migration smoke passed with PostGIS 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1 and required runtime schema objects present.
• Browser runtime verification passed for frontend, proxied API and icon.
• Live browser UI check against http://192.168.10.150:1202 passed on desktop and mobile: workflow guidance panel rendered with five steps, no horizontal overflow and no console warnings/errors.

Open:

• None known after full readiness, Tower deploy and live browser validation.

Limitations:

• Frontend Overview workflow guidance only; no API contract, persistence, migration, provider fetching or AI/model behavior changes.

Next recommended pass:

• After deploy and visual validation, continue with any remaining end-to-end workflow handoff polish surfaced by the live audit.

Sprint 91 populated workflow audit polish (2026-06-21)

Changed:

• Audited the live populated demo state on http://192.168.10.150:1202 across Overview, Data, Map, QA/QC, AI Labs and Exports.
• Changed the Overview workflow guidance badge to show Ready for handoff when project, dataset, map, QA/AI and export state are all present.
• Clarified the Map workflow step so it reports layer feature count separately from AOI context and shows AOI loaded for AOI-only map context.
• Extended backend/tests/test_sprint90_workflow_guidance.py with complete-state and Map-copy regression coverage.
• Updated frontend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Live pre-change browser audit passed for the populated demo workflow: Data had 2 ready datasets, Map rendered demo_predicted_buildings.geojson with 2 layer features, QA/QC showed 2 checks, AI Labs showed model/result/QA surfaces, Exports showed 50 artifacts, and no checked workspace had console warnings/errors or horizontal overflow.
• Red step: python -m pytest backend/tests/test_sprint90_workflow_guidance.py -q failed on missing workflowGuidanceComplete / Ready for handoff / precise Map copy contracts.
• Post-deploy live check found the AOI-only reload state rendered 0 layer features + AOI; a second red step failed on missing AOI loaded copy before the fix.
• python -m pytest backend/tests/test_sprint90_workflow_guidance.py -q (4 passed)
• python -m pytest backend/tests/test_sprint90_workflow_guidance.py backend/tests/test_sprint83_workspace_panel_hierarchy.py backend/tests/test_sprint82_shell_density_polish.py -q (10 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• Local browser UI check against http://127.0.0.1:5174 passed for the Overview guidance panel with no horizontal overflow. Local console showed expected Vite proxy 500s because only the frontend server was running.
• bash scripts/run_readiness_check.sh (307 passed; frontend typecheck/build passed; Alembic head 202606120900; live smoke syntax passed)
• Tower deploy via scripts/deploy_tower.ps1 rebuilt and restarted the all-in-one container on http://192.168.10.150:1202 from commit 97b943d.
• Tower deploy live migration smoke passed with PostGIS 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1 and required runtime schema objects present.
• Browser runtime verification passed for frontend, proxied API and icon.
• Live post-change browser check passed: AOI-only Overview state showed Map AOI loaded; after opening demo_predicted_buildings.geojson on the map, Overview showed Map 2 layer features + AOI; both states showed Ready for handoff, no horizontal overflow and no console warnings/errors.

Open:

• None known after full readiness, Tower deploy and live browser validation.

Limitations:

• Frontend Overview copy/state polish only; no API contract, persistence, migration, provider fetching or AI/model behavior changes.

Next recommended pass:

• After deploy and live validation, continue with any remaining populated-state visual issues found in the next audit.

Sprint 92 workflow rail interaction polish (2026-06-21)

Changed:

• Audited the live Overview workflow rail click path on http://192.168.10.150:1202.
• Added openWorkflowGuidanceStep so Map and Export rail clicks can reuse existing dataset context flows.
• Map rail click now opens the first ready vector/GeoJSON dataset through openDatasetInMap when no layer is active.
• Export rail click now opens the first ready vector/GeoJSON dataset through openDatasetExport when no dataset is selected.
• Extended backend/tests/test_sprint90_workflow_guidance.py with regression coverage for the context-aware rail handler.
• Updated frontend/README.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Live pre-change browser audit showed the rail navigated correctly but Map landed with no active layer and Exports landed with Selected dataset: none.
• Red step: python -m pytest backend/tests/test_sprint90_workflow_guidance.py -q failed on missing openWorkflowGuidanceStep / dataset-context handler contracts.
• python -m pytest backend/tests/test_sprint90_workflow_guidance.py backend/tests/test_sprint53_selection_ergonomics.py backend/tests/test_sprint67_map_empty_state_quick_actions.py backend/tests/test_sprint64_export_handoff_polish.py -q (15 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• Local browser UI check against http://127.0.0.1:5174 passed for rail rendering with no horizontal overflow. Local console showed expected Vite proxy 500s because only the frontend server was running.
• bash scripts/run_readiness_check.sh (308 passed; frontend typecheck/build passed; Alembic head 202606120900; live smoke syntax passed)
• Tower deploy from commit 1872c60 completed; all-in-one container published on 0.0.0.0:1202->80/tcp.
• Deploy-time live migration smoke passed after the database became ready on attempt 3; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1, required runtime schema objects were present and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for frontend, API proxy and icon.
• Live targeted browser validation against http://192.168.10.150:1202 passed for Open Map step: demo_predicted_buildings.geojson became active, 2 features rendered, no horizontal overflow and no console/page errors.
• Live targeted browser validation against http://192.168.10.150:1202 passed for Open Export step: demo_predicted_buildings.geojson became the selected export dataset, no horizontal overflow and no console/page errors.

Open:

• None for this pass.

Limitations:

• Frontend workflow navigation polish only; no API contract, persistence, migration, provider fetching or AI/model behavior changes.

Next recommended pass:

• After deploy and live validation, continue with export artifact action ergonomics or QA result drill-down, depending on the next live friction point.

Sprint 93 export handoff artifact polish (2026-06-21)

Changed:

• Added a Latest handoff artifacts section to frontend/src/components/exports/ExportCenter.tsx.
• Grouped newest persisted artifacts for project report, project metadata, dataset GeoJSON, detection GeoJSON and segmentation GeoJSON.
• Reused existing JSON preview and artifact download callbacks from latest artifact cards.
• Added responsive latest artifact card styling in frontend/src/styles/app.css.
• Added backend/tests/test_sprint93_export_handoff_completion.py to lock the grouped handoff surface, actions and responsive CSS.
• Updated CHANGELOG.md, docs/TODO.md and frontend/README.md.

Tested:

• Red step: python -m pytest backend\tests\test_sprint93_export_handoff_completion.py -q failed on missing latest artifact grouping/actions/styles.
• python -m pytest backend\tests\test_sprint93_export_handoff_completion.py -q (3 passed)
• python -m pytest backend\tests\test_sprint93_export_handoff_completion.py backend\tests\test_sprint64_export_handoff_polish.py backend\tests\test_sprint89_export_system_density.py backend\tests\test_sprint51_quality_export_polish.py backend\tests\test_sprint78_export_preview_readability.py -q (13 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh (311 passed; frontend typecheck/build passed; Alembic head 202606120900; live smoke syntax passed)
• Tower deploy from commit 0a9054c completed; all-in-one container published on 0.0.0.0:1202->80/tcp.
• Deploy-time live migration smoke passed after the database became ready on attempt 2; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1, required runtime schema objects were present and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for frontend, API proxy and icon.
• Live browser validation against http://192.168.10.150:1202 passed for Open Export step: selected dataset stayed demo_predicted_buildings.geojson, Latest handoff artifacts rendered, project report/project metadata/dataset GeoJSON cards were present, detection/segmentation cards showed No artifact yet, preview opened from the latest artifact card, no horizontal overflow and no console/page errors.

Open:

• None for this pass.

Limitations:

• Export Center frontend handoff polish only; no API contract, persistence, migration, provider fetching or AI/model behavior changes.

Next recommended pass:

• After deploy and live validation, continue with QA/QC drilldown and map evidence layers.

Sprint 94 QA/QC evidence drilldown (2026-06-22)

Changed:

• Added selected-check drilldown state to frontend/src/components/quality/QualityResultsPanel.tsx.
• Added a QA/QC evidence drilldown surface with selected check, candidate/reference layer, analysis run, job, status, score and timestamp provenance.
• Added false-positive evidence, false-negative evidence and map evidence handoff cards using persisted metric rows and dataset names.
• Added parameter and findings JSON panes for persisted QA/QC provenance.
• Added Inspect latest check and per-result Inspect check controls.
• Added responsive QA drilldown styles to frontend/src/styles/app.css.
• Added backend/tests/test_sprint94_quality_drilldown.py.
• Updated CHANGELOG.md, docs/TODO.md and frontend/README.md.

Tested:

• Red step: python -m pytest backend\tests\test_sprint94_quality_drilldown.py -q failed on missing drilldown state/surfaces/styles.
• python -m pytest backend\tests\test_sprint94_quality_drilldown.py backend\tests\test_sprint70_quality_handoff_polish.py backend\tests\test_sprint71_quality_metric_polish.py backend\tests\test_sprint73_quality_result_filtering.py backend\tests\test_sprint86_quality_workspace_density.py -q (15 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh (314 passed; frontend typecheck/build passed; Alembic head 202606120900; live smoke syntax passed)
• Tower deploy from commit f3ad9c3 completed; all-in-one container published on 0.0.0.0:1202->80/tcp.
• Deploy-time live migration smoke passed after the database became ready on attempt 2; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1, required runtime schema objects were present and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for frontend, API proxy and icon.
• Live browser validation against http://192.168.10.150:1202 passed for Open QA / AI step: QA/QC evidence drilldown rendered, candidate/reference names resolved, false-positive/negative evidence rendered, parameters/findings JSON rendered, per-result Inspect check controls were available, no horizontal overflow and no console/page errors.

Open:

• None for this pass.

Limitations:

• QA/QC frontend drilldown only; no API contract, persistence, migration, provider fetching or AI/model behavior changes.
• Map evidence handoff points reviewers to candidate/reference datasets already persisted in the workbench; it does not create a new matched/unmatched geometry export.

Next recommended pass:

• After deploy and live validation, continue with QA map overlay affordances or raster pipeline hardening.

Sprint 95 raster pipeline hardening (2026-06-22)

Changed:

• Added a Raster pipeline readiness surface to frontend/src/components/datasets/RasterControls.tsx.
• Surfaced metadata profile, CRS readiness, preview artifact, tile manifest handoff and clip AOI state before raster operations.
• Added Processing guardrails for missing selected dataset, unavailable raster processing, missing metadata, missing CRS, missing preview, invalid tile parameters and missing clip areas.
• Added responsive raster readiness, guardrail and manifest-handoff styles to frontend/src/styles/app.css.
• Added backend/tests/test_sprint95_raster_pipeline_hardening.py.
• Updated CHANGELOG.md, docs/TODO.md and frontend/README.md.

Tested:

• Red step: python -m pytest backend\tests\test_sprint95_raster_pipeline_hardening.py -q failed on missing readiness/handoff component structure and CSS.
• python -m pytest backend\tests\test_sprint95_raster_pipeline_hardening.py -q (2 passed)
• python -m pytest backend\tests\test_sprint80_operation_form_readability.py backend\tests\test_sprint77_inspector_mobile_polish.py -q (5 passed)
• python -m compileall backend/app
• cd backend && python -m pytest -q (316 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh (316 passed; frontend typecheck/build passed; Alembic head 202606120900; live smoke syntax passed)
• cd backend && python -m alembic heads (202606120900 (head))
• cd backend && python -m alembic upgrade head --sql
• bash -n scripts/live_migration_smoke.sh
• Tower deploy from commit 330b677 completed; all-in-one container published on 0.0.0.0:1202->80/tcp.
• Deploy-time live migration smoke passed after the database became ready on attempt 2; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1, required runtime schema objects were present and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for frontend, API proxy and icon.
• Live browser validation against http://192.168.10.150:1202 passed for the workbench shell: Overview/Data/Map/QA/QC/AI Labs/Exports markers were present, no horizontal overflow and no console/page errors.
• Live dataset API validation confirmed the default demo currently contains vector fixtures only, so the raster-specific panel is not visible in the default live state without a raster upload.

Open:

• None for this pass.

Limitations:

• Frontend raster inspector hardening only; no API contract, persistence, migration, provider fetching or AI/model behavior changes.
• The panel explains the existing tile manifest handoff but does not create a new manifest picker or new detection/segmentation behavior.
• Raster-specific live visual validation requires a raster dataset in the live project; the current offline demo seed contains vector fixtures only.

Next recommended pass:

• After deploy and live validation, add a raster fixture/demo upload smoke or continue with map evidence overlay affordances for QA/QC.

Sprint 96 useful default context (2026-06-22)

Changed:

• Added a guarded default dataset effect in frontend/src/hooks/useDatasetWorkflow.ts.
• When project data loads and no dataset is selected, the workbench now auto-opens the first ready vector dataset, falling back to any ready dataset and then the first dataset.
• This gives Data, Map and Exports an immediately useful selected dataset/map layer context for the populated demo flow.
• Added explicit no-raster guidance cards to Detection Lab and Segmentation Lab run controls.
• Added backend/tests/test_sprint96_useful_default_context.py.
• Updated CHANGELOG.md, docs/TODO.md and frontend/README.md.

Tested:

• Red step: python -m pytest backend\tests\test_sprint96_useful_default_context.py -q failed on missing default dataset selection and no-raster AI Lab guidance.
• python -m pytest backend\tests\test_sprint96_useful_default_context.py -q (2 passed)
• python -m pytest backend\tests\test_sprint39_frontend_orchestration_hooks.py backend\tests\test_sprint29_dataset_components.py backend\tests\test_sprint88_ai_lab_density.py -q (16 passed)
• python -m compileall backend/app
• cd backend && python -m pytest -q (318 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh (318 passed; frontend typecheck/build passed; Alembic head 202606120900; live smoke syntax passed)
• cd backend && python -m alembic heads (202606120900 (head))
• cd backend && python -m alembic upgrade head --sql
• bash -n scripts/live_migration_smoke.sh
• Tower deploy from commit aaf1299 completed; all-in-one container published on 0.0.0.0:1202->80/tcp.
• Deploy-time live migration smoke passed after the database became ready on attempt 3; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1, required runtime schema objects were present and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for frontend, API proxy and icon.
• Live browser validation against http://192.168.10.150:1202 passed: Data auto-selected demo_predicted_buildings.geojson, Map rendered 2 features, Exports showed the same selected dataset, AI Labs showed no-raster guidance for detection and segmentation, desktop/mobile had no horizontal overflow and no console/page errors.

Open:

• None for this pass.

Limitations:

• Frontend usability/default-context hardening only; no API contract, persistence, migration, provider fetching or AI/model behavior changes.
• Detection and segmentation remain correctly blocked without a raster dataset.

Next recommended pass:

• After deploy and live validation, add a raster fixture/demo upload smoke so AI Labs and raster controls can be validated with a connected raster state.

Sprint 101 AI Lab handoff browser smoke (2026-06-24)

Changed:

• Added scripts/verify_ai_handoff_interactions.sh.
• The script seeds the explicit offline demo workflow through the frontend-facing API, creates a small raster tile manifest, opens the browser workbench with Playwright/Chromium, clicks Use in Detection Lab and Use in Segmentation Lab, and verifies both AI Lab forms receive the selected raster dataset plus Raster tile manifest path.
• Added syntax coverage for the new script to scripts/run_readiness_check.sh.
• Added backend/tests/test_sprint101_ai_handoff_interaction_smoke.py.
• Updated scripts/README.md and CHANGELOG.md.

Tested:

• Red step: python -m pytest backend\tests\test_sprint101_ai_handoff_interaction_smoke.py -q failed while scripts/verify_ai_handoff_interactions.sh was absent.
• python -m pytest backend\tests\test_sprint101_ai_handoff_interaction_smoke.py -q (1 passed)
• bash -n scripts/verify_ai_handoff_interactions.sh
• Local Node Playwright availability check failed with ERR_MODULE_NOT_FOUND, so live browser-click execution requires Playwright to be installed or exposed in the runner environment.
• python -m compileall backend/app
• cd backend && python -m pytest -q (324 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh (324 passed; frontend typecheck/build passed; Alembic head 202606120900; live smoke syntax passed)
• cd backend && python -m alembic heads (202606120900 (head))
• cd backend && python -m alembic upgrade head --sql
• bash -n scripts/live_migration_smoke.sh
• Tower deploy from commit aab51ed completed; all-in-one container published on 0.0.0.0:1202->80/tcp.
• Deploy-time live migration smoke passed after the database became ready on attempt 3; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1, required runtime schema objects were present and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for frontend, API proxy and icon.
• Live smoke passed: bash scripts/verify_demo_raster_workflow.sh http://192.168.10.150:1202.
• Live smoke passed: bash scripts/verify_workbench_default_state.sh http://192.168.10.150:1202.
• Live smoke passed: bash scripts/verify_workbench_interactions.sh http://192.168.10.150:1202.
• Live smoke passed: bash scripts/verify_browser_runtime.sh http://192.168.10.150:1202.
• Live browser-click validation with Codex Playwright plus installed Chrome passed against http://192.168.10.150:1202: the smoke seeded the demo workflow, generated a raster tile manifest, selected the raster in Data, opened the Dataset inspector tab, clicked Use in Detection Lab, selected yolo-configured, verified Detection Lab dataset and manifest values, clicked Use in Segmentation Lab, verified Segmentation Lab dataset and manifest values, and observed no console/page errors.

Open:

• None for this pass.

Limitations:

• Test/smoke tooling only; no product behavior, API contract, migration, AI dependency, provider fetching or model behavior changes.
• scripts/verify_ai_handoff_interactions.sh requires a runner with Playwright plus a browser. This Codex environment had Playwright available through the Node REPL and used installed Chrome; plain local node still reports ERR_MODULE_NOT_FOUND for playwright.

Next recommended pass:

• After validation and deploy, keep using the AI handoff smoke as the browser-level regression guard for raster-to-AI workspace wiring.

Sprint 102 Detection Lab handoff polish (2026-06-24)

Changed:

• Updated useRasterTileManifestForDetection in frontend/src/App.tsx to set selectedDetectionModelId to yolo-configured when a raster tile manifest is handed off from the dataset inspector.
• Tightened scripts/verify_ai_handoff_interactions.sh so it verifies that Detection Lab is already on yolo-configured after the handoff instead of selecting that model inside the smoke.
• Updated backend/tests/test_sprint99_raster_ui_handoff.py and backend/tests/test_sprint101_ai_handoff_interaction_smoke.py.
• Updated CHANGELOG.md and docs/TODO.md.

Tested:

• Red step: python -m pytest backend\tests\test_sprint99_raster_ui_handoff.py -q failed while the detection handoff did not set yolo-configured.
• python -m pytest backend\tests\test_sprint99_raster_ui_handoff.py backend\tests\test_sprint101_ai_handoff_interaction_smoke.py -q (2 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash -n scripts/verify_ai_handoff_interactions.sh
• python -m compileall backend/app
• cd backend && python -m pytest -q (324 passed)
• bash scripts/run_readiness_check.sh (324 passed; frontend typecheck/build passed; Alembic head 202606120900; live smoke syntax passed)
• cd backend && python -m alembic heads (202606120900 (head))
• cd backend && python -m alembic upgrade head --sql
• bash -n scripts/live_migration_smoke.sh
• First Tower redeploy attempt failed with Docker btrfs no space left on device while writing build context files.
• Remote Docker diagnosis showed /var/lib/docker had ordinary free space but btrfs metadata was 94.80% used with the 300GB docker image fully allocated.
• Removed dangling Docker images and cleared orphaned hung Docker CLI processes from prior inspection/prune attempts.
• Ran a limited Docker btrfs balance: btrfs balance start -dusage=40 -musage=80 /var/lib/docker; metadata improved to 75.63% used and 94.95GiB became unallocated.
• Tower deploy from commit 4367400 then completed; all-in-one container published on 0.0.0.0:1202->80/tcp.
• Deploy-time live migration smoke passed after the database became ready on attempt 2; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1, required runtime schema objects were present and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for frontend, API proxy and icon.
• Live smoke passed: bash scripts/verify_demo_raster_workflow.sh http://192.168.10.150:1202.
• Live smoke passed: bash scripts/verify_workbench_default_state.sh http://192.168.10.150:1202.
• Live smoke passed: bash scripts/verify_workbench_interactions.sh http://192.168.10.150:1202.
• Live smoke passed: bash scripts/verify_browser_runtime.sh http://192.168.10.150:1202.
• Internal Codex browser validation passed against http://192.168.10.150:1202: Data workspace selected the raster fixture, Dataset inspector handoff clicked Use in Detection Lab, Detection Lab auto-selected yolo-configured, the raster dataset and tile manifest matched the generated manifest, the manifest input was visible, and no console/page errors were reported.

Open:

• None for this pass.

Limitations:

• Frontend handoff polish only; no backend API, persistence, migration, provider fetching, AI dependency or model execution behavior changed.
• The Tower Docker image required btrfs metadata balancing before redeploy. If this recurs, inspect btrfs filesystem usage /var/lib/docker; metadata near full can fail builds even when df reports free GB.

Next recommended pass:

• After live validation, continue with the next V1 usability gap from the workbench flow rather than adding new model/provider scope.

Sprint 103 AI Lab run readiness (2026-06-24)

Changed:

• Added compact run-readiness panels to Detection Lab and Segmentation Lab.
• Detection readiness now checks selected raster dataset, selected model availability and the configured-YOLO tile manifest requirement before a run is submitted.
• Segmentation readiness now checks selected raster dataset, configured segmentation model state and whether a tile manifest is present for provenance.
• Added shared AI Lab readiness styling and regression coverage in backend/tests/test_sprint103_ai_lab_run_readiness.py.
• Updated CHANGELOG.md and docs/TODO.md.

Tested:

• Red step: python -m pytest backend\tests\test_sprint103_ai_lab_run_readiness.py -q failed while the readiness panels and CSS contracts were absent.
• python -m pytest backend\tests\test_sprint103_ai_lab_run_readiness.py -q (3 passed)
• python -m compileall backend/app
• cd backend && python -m pytest -q (327 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh (327 passed; frontend typecheck/build passed; Alembic head 202606120900; live smoke syntax passed)
• cd backend && python -m alembic heads (202606120900 (head))
• cd backend && python -m alembic upgrade head --sql
• bash -n scripts/live_migration_smoke.sh
• Tower deploy from commit 98fff63 completed; all-in-one container published on 0.0.0.0:1202->80/tcp.
• Deploy-time live migration smoke passed after the database became ready on attempt 3; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1, required runtime schema objects were present and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for frontend, API proxy and icon.
• Live smoke passed: bash scripts/verify_demo_raster_workflow.sh http://192.168.10.150:1202.
• Live smoke passed: bash scripts/verify_workbench_default_state.sh http://192.168.10.150:1202.
• Live smoke passed: bash scripts/verify_workbench_interactions.sh http://192.168.10.150:1202.
• Live smoke passed: bash scripts/verify_browser_runtime.sh http://192.168.10.150:1202.
• Internal Codex browser validation passed against http://192.168.10.150:1202: AI Labs rendered Detection and Segmentation run-readiness panels; Detection stayed blocked when Configured YOLO detector had no local YOLO configuration but marked dataset and tile manifest as provided; Segmentation became ready with the explicit fixture segmenter and manifest; desktop and mobile viewports had no horizontal overflow and no console warnings/errors.

Open:

• None for this pass.

Limitations:

• Frontend readiness guidance only; no backend API, persistence, migration, provider fetching, AI dependency or model execution behavior changed.

Next recommended pass:

• Continue with V1 usability work that reduces operator confusion without expanding frozen product scope.

Sprint 122 Detection model asset activation guardrails (2026-07-08)

Changed:

• Hardened Detection Lab local model handling so reported runtime model assets are read-only choices and are not auto-selected by the frontend hook.
• Configured YOLO run readiness now blocks submission when local model assets exist but no explicit model_asset_id has been selected.
• Added an explicit model asset section with active runtime env status, will_download_models, SHA-256 preview, file size and mounted model path.
• Surfaced the current benchmark candidate geointel-building-yolov8s-hardneg160r4e50-pt with recommended starting threshold 0.25.
• Added compact UI guidance styling for the benchmark/threshold warning.
• Added regression coverage in backend/tests/test_sprint122_model_asset_activation_guardrails.py.
• Updated CHANGELOG.md and docs/TODO.md.

Tested:

• Red step: python -m pytest backend\tests\test_sprint122_model_asset_activation_guardrails.py -q failed on the previous auto-selection behavior and missing guardrail copy.
• python -m pytest backend\tests\test_sprint122_model_asset_activation_guardrails.py -q (3 passed)
• python -m pytest backend\tests\test_sprint118_yolo_preflight_ui.py backend\tests\test_sprint103_ai_lab_run_readiness.py backend\tests\test_sprint104_ai_lab_action_guardrails.py backend\tests\test_model_asset_catalog.py backend\tests\test_sprint122_model_asset_activation_guardrails.py -q (16 passed)
• python -m compileall backend/app
• python -m pytest backend\tests -q (408 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build

Open:

• Full threshold calibration comparison UI is still pending; this pass adds safe single-threshold guidance and explicit asset choice only.

Limitations:

• Frontend guardrail only; no backend API contracts, migrations, provider fetching, model downloads or model weight mutation behavior changed.
• The active runtime env model can still be configured by operators through existing deployment/env tooling, but the Detection Lab no longer silently chooses a local asset from the catalog for a run.

Next recommended pass:

• Add threshold calibration comparison UX over existing persisted runs so candidate models can be promoted with visible precision/recall/F1 and hard-negative counts.

Operator YOLOv8s hard-negative model benchmark (2026-07-08)

Changed:

• No repository code, API contract, migration, UI or application behavior was changed in this pass.
• Downloaded the official Ultralytics YOLOv8s base model manually as an operator/runtime asset on Tower at /mnt/user/appdata/geointel/models/yolov8s.pt.
• Trained a local hard-negative building detector on Tower from the existing exported operator tile dataset yolo-building-tile-hardneg160r4.
• Produced the trained runtime model artifact /mnt/user/appdata/geointel/models/geointel-building-yolov8s-hardneg160r4e50.pt, mounted in the container as /app/models/geointel-building-yolov8s-hardneg160r4e50.pt.
• Verified the live model catalog exposes the trained model as geointel-building-yolov8s-hardneg160r4e50-pt with SHA256 9bf71ad4742048ac77f07060b677bacd9757b8d310497fcada334d543e320d19, size 22473194, will_download_models=false and active=false.
• Reused existing persisted dense QA and hard-negative benchmark runs through the live API; no external provider fetching and no fake data were introduced.

Training evidence:

• Base model: /app/models/yolov8s.pt.
• Base model SHA256: 1f47a78bf100391c2a140b7ac73a1caae18c32779be7d310658112f7ac9aa78a.
• Training run: /app/storage/training/operator-yolo/geointel-building-yolov8s-hardneg160r4e50.
• Best checkpoint copied to: /app/models/geointel-building-yolov8s-hardneg160r4e50.pt.
• Final validation from Ultralytics: precision 0.449, recall 0.404, mAP50 0.322, mAP50-95 0.110.

Live benchmark evidence:

• Created 24 persisted rescored detection runs with model_asset_id=geointel-building-yolov8s-hardneg160r4e50-pt.
• Dense persisted QA matrix results at IoU 0.1:
  • Four dense benchmark scenes saturated at 300 detections across thresholds 0.05, 0.15 and 0.25.
  • Dense F1 scores observed: 0.6380, 0.5627, 0.5247, 0.4678.
  • Dense precision ranged from 0.7167 to 0.8600; recall ranged from 0.3247 to 0.5749.
  • Mean IoU ranged from 0.3962 to 0.4465.
  • The sparse/forest scene improved as the threshold increased: 49 detections and F1 0.1429 at 0.05; 18 detections and F1 0.3200 at 0.15; 10 detections and F1 0.4706 at 0.25.
• Hard-negative detection counts:
  • kasterlee_bos: 49 at threshold 0.05, 18 at 0.15, 10 at 0.25.
  • lommel_heide: 0 at thresholds 0.05, 0.15 and 0.25.
  • postel_bos: 1 at threshold 0.05, 0 at 0.15 and 0.25.

Assessment:

• The YOLOv8s hard-negative model is a materially better evaluation candidate than the previous tiny smoke models and is useful in dense building scenes.
• The model should not be made the silent default yet because kasterlee_bos still produces 10 hard-negative detections at threshold 0.25.
• Threshold 0.25 is the safest observed operating point for the current candidate.
• The next product step should be model-selection and threshold workflow hardening before any operator-facing default activation.

Operational note:

• Tower root SSH works with widefrog_unraid_deploy on the default SSH port 22.
• Gitea SSH works separately on port 222 through the gitea-widefrog host alias and widefrog_gitea key.
• Do not test Unraid/root deploy access against port 222; that port belongs to Gitea and correctly rejects root.
• Live HTTP API verification on http://192.168.10.150:1202 remained reachable.

Open:

• Add an operator-facing model selection/activation flow or documented command that can promote a chosen local model deliberately, with visible SHA256 and threshold guidance.
• Add more negative/background AOIs and a threshold calibration benchmark before activating this model by default.

Next recommended pass:

• Implement V1 model-catalog hardening: show available local model assets, make active model/threshold explicit, and prevent accidental silent default activation.

Sprint 123 Raster detection manifest handoff (2026-07-08)

Changed:

• Added a typed RasterTileHandoff frontend contract so raster tile jobs can expose manifest path, tile count, tile size, overlap, tile-set id and source dataset provenance.
• Updated the dataset workflow hook to retain the latest structured raster tile manifest alongside the existing manifest path.
• Updated Raster Controls to show manifest details and explicit Detection/Segmentation Lab handoff actions.
• Updated the Detection Lab handoff so a raster manifest selects yolo-configured, selects the active raster dataset, applies threshold 0.25, refreshes YOLO preflight and does not auto-select a local model asset.
• Added linked tile manifest, manifest validation, tile count and will_run_inference visibility to Detection Lab.
• Added regression coverage in backend/tests/test_sprint123_raster_detection_handoff_operational.py.

Tested:

• Red step: python -m pytest backend\tests\test_sprint123_raster_detection_handoff_operational.py -q failed while the typed handoff, Raster Controls details and Detection Lab preflight indicators were absent.
• python -m pytest backend\tests\test_sprint123_raster_detection_handoff_operational.py -q (3 passed)
• python -m pytest backend\tests\test_sprint99_raster_ui_handoff.py backend\tests\test_sprint122_model_asset_activation_guardrails.py backend\tests\test_sprint120_model_asset_detection_workflow_smoke.py backend\tests\test_sprint123_raster_detection_handoff_operational.py -q (8 passed)
• python -m compileall backend/app
• cd frontend && npm run typecheck
• cd frontend && npm run build

Limitations:

• This pass improves the operator handoff and preflight visibility only. It does not change backend inference contracts, migrations, model files, provider fetching or automatic model promotion.

Next recommended pass:

• Add threshold calibration comparison UX so an operator can compare candidate thresholds before promoting a local model.

Sprint 133 Detection threshold calibration UX (2026-07-08)

Changed:

• Added a Detection Lab calibration comparison panel that combines existing persisted DetectionRunRead rows with existing persisted QualityCheckRead/metric rows.
• The panel shows confidence threshold, model, local model asset id, detection count, precision, recall, F1, false positives, false negatives and linked quality-check id.
• Added summary cards for best F1 candidate, best precision candidate and lowest false-positive pressure.
• Added a promotion guardrail that keeps model/threshold acceptance tied to QA evidence across AOIs instead of a single run.
• Passed project-level qualityChecks into Detection Lab without adding API routes, migrations or new AI execution behavior.
• Added regression coverage in backend/tests/test_sprint133_detection_threshold_calibration_ux.py.
• Marked the threshold calibration UX item complete in docs/TODO.md.

Tested:

• Red step: python -m pytest backend\tests\test_sprint133_detection_threshold_calibration_ux.py -q failed while the persisted calibration comparison UI was absent.
• python -m pytest backend\tests\test_sprint133_detection_threshold_calibration_ux.py -q (1 passed)
• python -m pytest backend\tests\test_sprint122_model_asset_activation_guardrails.py backend\tests\test_sprint123_raster_detection_handoff_operational.py backend\tests\test_sprint133_detection_threshold_calibration_ux.py -q (7 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build

Limitations:

• This is a persisted-run comparison surface only. It does not launch batch calibration sweeps from the browser and does not auto-promote model assets or thresholds.

Next recommended pass:

• Add a guided in-app calibration runner that can queue a small explicit threshold set for one selected raster/reference pair, reusing the existing detection and QA APIs.

Sprint 134 Guided detection calibration runner (2026-07-08)

Changed:

• Added an explicit guided calibration runner to Detection Lab for operator-selected confidence threshold sweeps.
• Added frontend detection workflow state for calibration thresholds, progress rows, running state and errors.
• The runner parses a space/comma/semicolon-separated threshold set, validates project/raster/reference/model/manifest/model-asset readiness and runs one existing detectionApi.run plus one existing detection QA comparison per threshold.
• Successful threshold rows report persisted analysis run/job/quality check ids, detection count, precision, recall, F1, false positives and false negatives.
• Added UI copy that this runs real configured YOLO jobs and QA comparisons and does not promote or mutate model files.
• Added regression coverage in backend/tests/test_sprint134_guided_detection_calibration_runner.py.
• Marked the guided calibration runner task complete in docs/TODO.md.

Tested:

• Red step: python -m pytest backend\tests\test_sprint134_guided_detection_calibration_runner.py -q failed while the runner contract was absent.
• python -m pytest backend\tests\test_sprint134_guided_detection_calibration_runner.py -q (1 passed)
• python -m pytest backend\tests\test_sprint133_detection_threshold_calibration_ux.py backend\tests\test_sprint134_guided_detection_calibration_runner.py backend\tests\test_sprint122_model_asset_activation_guardrails.py backend\tests\test_sprint123_raster_detection_handoff_operational.py -q (8 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build

Limitations:

• The runner is intentionally sequential and explicit. It does not schedule background batches, compare multiple AOIs at once or promote model assets/thresholds automatically.

Next recommended pass:

• Add an evidence shortcut from each calibration row to the persisted QA evidence map/review flow so false positives and false negatives can be inspected faster.

Sprint 117 Safe local YOLO model activation (2026-07-06)

Changed:

• Added scripts/configure_yolo_model.py to configure an existing local YOLO model into the deployment .env file without downloading model weights, loading a model or running inference.
• The helper scans a mounted model directory for .pt, .onnx and .engine files, refuses no-model and ambiguous multi-model states, and writes env updates only when --apply is provided.
• Added regression coverage in backend/tests/test_sprint119_yolo_model_configuration.py for no local model, ambiguous model selection, dry-run single model selection and env-file apply behavior.
• Downloaded the official Ultralytics yolov8n.pt smoke model to Tower under /mnt/user/appdata/geointel/models/yolov8n.pt, recorded checksum f59b3d833e2ff32e194b5bb8e08d211dc7c5bdf144b90d2c8412c47ccfc83b36, and applied the env configuration with the local helper.
• Hardened YoloDetectionAdapter.predict_tile so non-RGB raster tile artifacts are converted to a temporary RGB image before YOLO inference while georeferencing remains driven by the tile manifest.
• Wrapped YOLO prediction runtime errors as typed DETECTION_INFERENCE_FAILED AppErrors instead of leaking raw runtime exceptions through FastAPI.
• Updated scripts/README.md, deploy/unraid/README.md, backend/README.md, docs/AI_PIPELINES.md, docs/TODO.md and CHANGELOG.md.

Tested:

• Red step: python -m pytest backend\tests\test_sprint119_yolo_model_configuration.py -q failed while scripts/configure_yolo_model.py was absent.
• Red step: python -m pytest backend\tests\test_sprint8b_yolo_foundation.py -q failed because single-band TIFF tiles were passed through as mode L and prediction runtime errors leaked as raw RuntimeError.
• python -m pytest backend\tests\test_sprint119_yolo_model_configuration.py -q (4 passed)
• python -m pytest backend\tests\test_sprint8b_yolo_foundation.py -q (12 passed)
• python -m py_compile scripts\configure_yolo_model.py
• python -m compileall backend/app
• cd backend && python -m pytest -q (377 passed, existing Pydantic protected-namespace warnings remain)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh (Run readiness check passed)
• cd backend && python -m alembic heads (202606120900 (head))
• cd backend && python -m alembic upgrade head --sql
• bash -n scripts/live_migration_smoke.sh
• bash -n scripts/deploy_tower.sh
• Tower deploy from commit 72ee623 completed with GEOINTEL_INSTALL_AI=true; the all-in-one container is published on 0.0.0.0:1202->80/tcp.
• Deploy-time live migration smoke passed after the database became ready on attempt 2; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1, required runtime schema objects were present and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for frontend, API proxy and icon.
• Live API check passed: GET /api/v1/detection/yolo/preflight returned canonical data with status=not_configured, YOLO_ENABLED=false, torch_version=2.12.1, ultralytics_version=8.4.89, will_download_models=false and will_run_inference=false.
• Tower helper dry-run passed: python scripts/configure_yolo_model.py --models-dir /mnt/user/appdata/geointel/models --env-file .env --json returned status=no_model_found, empty candidates and no env updates.
• Tower model apply passed: python scripts/configure_yolo_model.py --models-dir /mnt/user/appdata/geointel/models --env-file .env --model-file /mnt/user/appdata/geointel/models/yolov8n.pt --apply --json returned status=applied, YOLO_ENABLED=true and YOLO_MODEL_PATH=/app/models/yolov8n.pt.
• Live YOLO preflight with generated demo raster tile manifest passed with status=ready, model_load_ok=true, manifest_valid=true, tile_paths_exist=true, tile_count=1, will_download_models=false and will_run_inference=false.
• Live inference smoke before the RGB adapter fix reproduced the runtime bug: YOLOv8n expected 3 channels but the demo tile was single-band (input[1, 1, 480, 640]).
• Tower deploy from commit add4768 completed with GEOINTEL_INSTALL_AI=true; the all-in-one container is published on 0.0.0.0:1202->80/tcp.
• Deploy-time live migration smoke passed after the database became ready on attempt 3; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1, required runtime schema objects were present and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for frontend, API proxy and icon.
• Live YOLO preflight after the RGB adapter fix passed with generated demo raster tile manifest and status=ready.
• Live real YOLO inference smoke after the RGB adapter fix passed: POST /api/v1/detection/run returned status=success, analysis run bbd80690-baf3-4e83-8ffb-fdcb07a70c0c, job 9636ed10-bbde-463f-a47a-6d7706c81e19, detection_count=0 and no error code. Zero detections is expected for the generic COCO smoke model on the synthetic demo raster.
• Downloaded keremberke/yolov8n-building-segmentation from Hugging Face as an explicit Tower runtime artifact at /mnt/user/appdata/geointel/models/yolov8n-building-segmentation.pt.
• Recorded building-model checksum 152d6a9c5c76c9f2fd2fd5cc167efaed7c8c02e31002b15415899710f1d71f98, matching the Hugging Face file metadata for best.pt.
• Applied .env with YOLO_MODEL_PATH=/app/models/yolov8n-building-segmentation.pt through scripts/configure_yolo_model.py --model-file ... --apply.
• Restarted the all-in-one container without rebuild; it remained healthy and published on 0.0.0.0:1202->80/tcp.
• Live building-model preflight passed with model_load_ok=true, dependencies_available=true, torch_version=2.12.1, ultralytics_version=8.4.89, will_download_models=false and will_run_inference=false.
• Live building-model preflight with generated demo raster tile manifest passed with status=ready.
• Live real building-model YOLO inference smoke passed: POST /api/v1/detection/run returned status=success, analysis run be609d4a-3e01-42df-bca8-0d5a2289120d, job 9c8bc26d-7753-4537-ab98-dc31b9029450, detection_count=0 and no error code. Zero detections remains expected on the synthetic demo raster.

Open:

• A local YOLO smoke model is now present and configured on Tower, but it is the generic COCO yolov8n.pt model. It proves the runtime path, not production-quality aerial building detection.

Limitations:

• The bundled Tower model file was placed as an operator/runtime artifact under appdata, not committed to Git.
• The configured model is a generic COCO model and should be replaced by a suitable aerial/building detector for meaningful GIS output.
• The active configured model is now a building segmentation YOLO model trained for satellite-building segmentation, but it is still a third-party Hugging Face PyTorch .pt artifact and should be treated as an evaluation model until validated on local Belgian/Kempen orthophotos.
• No model download behavior was added to the application; the manual operator placement remains explicit.
• If multiple local model files are present, the operator must choose one with --model-file so GeoIntel does not silently activate the wrong model.

Next recommended pass:

• Load a real georeferenced orthophoto/GeoTIFF for the target area, generate raster tiles, run the active building YOLO model and compare persisted detections against reference vector buildings through QA/QC.

Sprint 104 AI Lab action guardrails (2026-06-24)

Changed:

• Added explicit action guardrails below the Detection Lab and Segmentation Lab run-readiness panels.
• Detection now distinguishes configured model registry state from UI-runnable action state, blocking the explicit test/demo-only manual-fixture-detector in the normal workbench run form.
• Segmentation now distinguishes configured model registry state from UI-runnable action state, blocking the explicit test/demo-only fixture-segmenter in the normal workbench run form.
• Updated run button disabled conditions to use the new readiness/action state.
• Added compact guardrail styling and regression coverage in backend/tests/test_sprint104_ai_lab_action_guardrails.py.
• Updated CHANGELOG.md and docs/TODO.md.

Tested:

• Red step: python -m pytest backend\tests\test_sprint104_ai_lab_action_guardrails.py -q failed while the action guardrails and CSS contracts were absent.
• python -m pytest backend\tests\test_sprint104_ai_lab_action_guardrails.py -q (3 passed)
• python -m pytest backend\tests\test_sprint103_ai_lab_run_readiness.py backend\tests\test_sprint104_ai_lab_action_guardrails.py -q (6 passed)
• python -m compileall backend/app
• cd backend && python -m pytest -q (330 passed)
• cd frontend && npm run typecheck
• cd frontend && npm run build
• bash scripts/run_readiness_check.sh (330 passed; frontend typecheck/build passed; Alembic head 202606120900; live smoke syntax passed)
• cd backend && python -m alembic heads (202606120900 (head))
• cd backend && python -m alembic upgrade head --sql
• bash -n scripts/live_migration_smoke.sh
• Tower deploy from commit 0bc3b2f completed; all-in-one container published on 0.0.0.0:1202->80/tcp.
• Deploy-time live migration smoke passed after the database became ready on attempt 2; PostGIS reported 3.6 USE_GEOS=1 USE_PROJ=1 USE_STATS=1, required runtime schema objects were present and Alembic head was 202606120900.
• Deploy-time browser runtime verification passed for frontend, API proxy and icon.
• Live smoke passed: bash scripts/verify_demo_raster_workflow.sh http://192.168.10.150:1202.
• Live smoke passed: bash scripts/verify_workbench_default_state.sh http://192.168.10.150:1202.
• Live smoke passed: bash scripts/verify_workbench_interactions.sh http://192.168.10.150:1202.
• Live smoke passed: bash scripts/verify_browser_runtime.sh http://192.168.10.150:1202.
• Internal Codex browser validation passed against http://192.168.10.150:1202: AI Labs opened cleanly; run action guardrails rendered; Detection and Segmentation run buttons were disabled without a raster dataset; selecting explicit fixture models kept both run buttons disabled with Fixture model is explicit test/demo-only and Fixture segmenter is explicit test/demo-only; desktop and mobile viewports had no horizontal overflow and no console warnings/errors.

Open:

• None for this pass.

Limitations:

• Frontend action-guardrail guidance only; no backend API, persistence, migration, provider fetching, AI dependency or model execution behavior changed.

Next recommended pass:

• Continue with V1 usability work that reduces operator confusion without expanding frozen product scope.

Sprint 142 Calibration evidence response uniqueness (2026-07-08)

Changed:

• Hardened scripts/export_detection_calibration_evidence.sh so calibration evidence response files include both confidence threshold and quality-check id.
• Prevented same-threshold runs from different model assets or calibration jobs from overwriting each other before portfolio assembly.
• Extended the multi-AOI calibration evidence portfolio regression test with two Geel runs at the same threshold and distinct quality checks.

Tested:

• Red step: python -m pytest backend\tests\test_sprint139_multi_aoi_calibration_evidence_portfolio.py -q failed because only two evidence features were retained when three same-threshold responses were expected.
• python -m pytest backend\tests\test_sprint139_multi_aoi_calibration_evidence_portfolio.py backend\tests\test_sprint138_calibration_evidence_bundle_smoke.py backend\tests\test_sprint137_browser_calibration_summary_evidence_script.py backend\tests\test_sprint125_detection_calibration_evidence_bundle.py -q (4 passed)
• bash -n scripts/export_detection_calibration_evidence.sh
• bash -n scripts/assemble_detection_calibration_evidence_portfolio.sh
• python scripts\smoke_docs.py
• python -m compileall backend/app
• bash scripts/run_readiness_check.sh (418 passed, frontend typecheck/build passed, Alembic head 202606120900)
• Tower pulled commit 5314f16 and regenerated /mnt/user/appdata/geointel/artifacts/detection-calibration-portfolio/positive-aoi-expanded-20260708/output.
• The regenerated portfolio contains 7 samples, 38,007 evidence features and no missing model metadata.
• Best runs are now preserved across same-threshold model comparisons: Geel, Mol, Turnhout, Retie, Balen, Herentals and Westerlo all select geointel-building-yolov8n-expanded160e50-pt as their best scored run.
• Tower container remained healthy on 0.0.0.0:1202->80/tcp.

Open:

• The expanded160e50 model is consistently best on the current positive AOI portfolio, but hard-negative/background AOI evidence still prevents blind default promotion.

Sprint 146 - Operator YOLO dataset quality audit

What changed

• Added scripts/audit_operator_yolo_dataset_quality.py to inspect generated operator YOLO tile datasets before further model training.
• Added a focused pytest that creates a synthetic tile summary and YOLO label files, then verifies JSON/Markdown audit output and warning gates.
• Added the audit script to the readiness syntax gate.
• Documented the operator audit command in scripts/README.md.

What was tested

• python -m pytest backend\tests\test_sprint146_operator_yolo_dataset_quality_audit.py -q
• bash scripts/run_readiness_check.sh
• Tower live audit after pulling commit 5898e54:
  • yolo-building-tile-dataset: needs_attention; only 3 positive samples and no background negatives.
  • yolo-building-tile-expanded160: ok; 10 samples, 8 positive samples, 360 tiles, 11,213 labels, no missing/invalid label rows.
  • yolo-building-tile-hardneg160r4: needs_attention; repeated background negatives are 91.1% of negative tiles.
  • yolo-building-tile-hardneg160r8: needs_attention; repeated background negatives are 95.4% of negative tiles.

Known limitations

• The audit is evidence tooling only. It does not modify datasets, train models, fetch external data or change active YOLO configuration.
• The report flags likely dataset risks, but final promotion decisions must still come from persisted detection QA/QC matrices and hard-negative benchmarks.

Next recommended pass

• Add more unique hard-negative/background AOIs before another hard-negative training run. The current label files are clean, so the bottleneck is dataset diversity and balance rather than label-file corruption.

Sprint 147 - Unique hard-negative AOI expansion

What changed

• Expanded scripts/prepare_operator_real_data_samples.py with six additional explicit background-candidate AOIs: Dessel-heide, Ravels-bos, Meerhout-bos, Geel-Bel, Arendonk-heide and Herenthout-bos.
• Background candidates remain operator/runtime samples only. They are not product providers, not fixtures and not automatic app fetches.
• Added sample-registry test coverage for minimum unique background count, unique centers and regional spread.
• Updated operator documentation with the expanded default corpus and the next required Tower regeneration step.
• Fixed the all-in-one Dockerfile so the documented operator scripts are copied into /app/scripts/ during normal rebuilds.
• Fixed the all-in-one Dockerfile training-runtime gap so /app/scripts/train_operator_yolo_detector.sh is also copied into the rebuilt container and marked executable.
• Fixed scripts/yolo_preflight.py so it respects YOLO_ENABLED, YOLO_MODEL_PATH and YOLO_MAX_TILES from the runtime environment unless explicit CLI overrides are supplied.
• Prepared the expanded Tower operator manifest and exported yolo-building-tile-uniquehardneg160.

What was tested

• python -m pytest backend\tests\test_sprint131_operator_sample_expansion.py -q
• python -m pytest backend\tests\test_sprint127_operator_sample_quality_matrix.py backend\tests\test_sprint131_operator_sample_expansion.py -q
• python -m pytest backend\tests\test_sprint13_yolo_preflight.py -q
• Red/green Docker runtime guard for the training wrapper: python -m pytest backend\tests\test_docker_runtime_config.py::test_all_in_one_dockerfile_copies_operator_scripts_for_runtime_use -q
• python -m pytest backend\tests\test_docker_runtime_config.py -q
• bash scripts/run_readiness_check.sh
• Tower live operator sample prep:
  • manifest samples: 16 total, 7 reference and 9 background candidates.
  • background candidate GRB feature counts: Postel-bos 0, Lommel-heide 0, Kasterlee-bos 7, Dessel-heide 30, Ravels-bos 3, Meerhout-bos 20, Geel-Bel 17, Arendonk-heide 0, Herenthout-bos 90.
• Tower live tile export and audit for yolo-building-tile-uniquehardneg160:
  • status ok
  • 576 tiles, 346 positive, 230 negative
  • 16 samples, 13 positive samples, 9 background samples
  • 11,757 labels, 0 missing label files, 0 invalid label rows
  • repeated background negative share 0.0
• Tower all-in-one rebuild from commit f949347 with GEOINTEL_INSTALL_AI=true:
  • live migration smoke passed.
  • browser runtime verification passed on http://192.168.10.150:1202.
  • /app/scripts/prepare_operator_real_data_samples.py, /app/scripts/export_operator_yolo_tile_dataset.py and /app/scripts/audit_operator_yolo_dataset_quality.py are present in the rebuilt container.
  • YOLO preflight with an existing raster tile manifest returned status: ready, dependencies_available: true, model_file_exists: true, tile_paths_exist: true, will_download_models: false and will_run_inference: false.

Known limitations

• Some background candidates contain real GRB buildings. They are still useful as mixed rural/background samples, but the pure negative pressure currently comes mostly from Postel-bos, Lommel-heide and Arendonk-heide plus empty tiles inside sparse candidates.
• The rebuilt Tower image now contains the operator scripts automatically. The generated yolo-building-tile-uniquehardneg160 dataset is ready for a controlled training candidate, but no model has been promoted from it yet.

Next recommended pass

• Use yolo-building-tile-uniquehardneg160 as the next safer hard-negative training dataset candidate. Benchmark after training before changing defaults.

Sprint 152 - GRB reference paging for operator samples

What changed

• Fixed scripts/prepare_operator_real_data_samples.py so GRB GBG reference exports follow OGC API rel=next pagination links instead of silently trusting only the first limit=1000 page.
• Added operator controls:
  • --reference-page-limit / OPERATOR_GRB_PAGE_LIMIT, default 1000.
  • --reference-max-features / OPERATOR_GRB_MAX_FEATURES, default 100000.
• Generated reference GeoJSON now records source_urls, reference_pages_fetched, reference_truncated, reference_page_limit and reference_max_features.
• Kept the change operator-only: no GeoIntel API route calls this helper, no product provider endpoint changed, no live GRB/OSM import was added, no migration changed and no YOLO model was activated.

What was tested

• RED: python -m pytest backend\tests\test_sprint131_operator_sample_expansion.py::test_fetch_reference_follows_grb_next_links_until_complete -q failed because only the first GRB page was persisted.
• GREEN: python -m pytest backend\tests\test_sprint131_operator_sample_expansion.py::test_fetch_reference_follows_grb_next_links_until_complete -q
• python -m pytest backend\tests\test_sprint127_operator_sample_quality_matrix.py backend\tests\test_sprint131_operator_sample_expansion.py -q
• python -m py_compile scripts\prepare_operator_real_data_samples.py
• bash scripts/run_readiness_check.sh
• Local live GRB paging smoke with page_limit=2 and max_features=5 fetched 3 source pages, returned 5 features and correctly marked reference_truncated=true.
• Tower deploy from commit a63d4ea passed live migration smoke and browser runtime verification on http://192.168.10.150:1202.
• Tower one-sample Geel AOI1024 paging smoke passed with /opt/geointel/venv/bin/python and produced 2,268 GRB reference features instead of the old 1,000-feature cap.
• Tower full AOI1024 operator sample regeneration passed:
  • Geel: 2,268 features, 3 GRB pages, not truncated.
  • Mol: 1,993 features, 2 GRB pages, not truncated.
  • Turnhout: 3,278 features, 4 GRB pages, not truncated.
  • Herentals: 2,478 features, 3 GRB pages, not truncated.
  • Balen: 1,343 features, 2 GRB pages, not truncated.
  • Retie: 1,734 features, 2 GRB pages, not truncated.
  • Westerlo: 1,133 features, 2 GRB pages, not truncated.
• Re-exported /app/storage/operator-data/yolo-building-aoi1024-visible025 from the regenerated paged references:
  • 144 tiles, 117 positive tiles, 27 negative tiles, 29,170 labels.
  • min_label_visible_ratio=0.25, negative_keep_ratio=1.0.
• Re-ran the Tower dataset audit:
  • output: /app/storage/operator-data/yolo-building-aoi1024-visible025-audit/operator_yolo_dataset_quality_audit.json
  • status remains needs_attention
  • missing label files: 0
  • invalid labels: 0
  • median normalized box area: 0.000762939453125
  • small-box share: 0.34744600617072335
  • repeated background negative share: 0.0

Known limitations

• The Tower AOI1024 operator samples and tile dataset have now been regenerated from paged GRB references, but the tile audit still reports needs_attention because small clipped boxes remain common.
• This does not make the Sprint 7B GRB provider a live product importer; it only fixes explicit operator sample preparation.

Next recommended pass

• Before another training run, improve label quality: either raise --min-label-visible-ratio, increase tile size, reduce dense clipped-edge tiles or add cleaner AOIs. Do not activate or retrain from this dataset as-is without acknowledging the small-box warning.

Sprint 151 - Runtime GIS upload and AOI1024 YOLO candidate

What changed

• Fixed scripts/train_operator_yolo_detector.sh so the all-in-one image uses /opt/geointel/venv/bin/python by default when that AI venv exists. Explicit PYTHON_BIN still wins, and local shells still fall back to python3.
• Raised the Nginx request body limit to 250m in both frontend/nginx.conf and deploy/unraid/nginx-all-in-one.conf after the live 1024px GeoTIFF upload path returned 413 Request Entity Too Large.
• Raised Nginx proxy read/send timeouts to 600s after the live low-threshold persisted YOLO/QA path reached 504 Gateway Timeout.
• Kept the change runtime-only: no API contract, persistence model, migration, model-download behavior or default model selection changed.

What was tested

• Red/green TDD guard for the training wrapper fallback:
  • python -m pytest backend\tests\test_sprint129_operator_yolo_training_dataset.py -q
• Red/green TDD guard for real GIS upload payload support:
  • python -m pytest backend\tests\test_docker_runtime_config.py::test_nginx_runtime_allows_real_gis_upload_payloads -q
• Red/green TDD guard for long AI/QA proxy requests:
  • python -m pytest backend\tests\test_docker_runtime_config.py::test_nginx_runtime_allows_long_ai_and_qa_requests -q
• bash -n scripts/train_operator_yolo_detector.sh
• Tower live model training on /app/storage/operator-data/yolo-building-aoi1024-visible025:
  • output model: /app/models/geointel-building-yolov8s-aoi1024visible025e50.pt
  • model asset id: geointel-building-yolov8s-aoi1024visible025e50-pt
  • final Ultralytics validation: precision approximately 0.275, recall 0.331, mAP50 0.188, mAP50-95 0.0716
• Tower live runtime validation after deploy:
  • browser/runtime verification passed on http://192.168.10.150:1202
  • the previous Geel 1024px upload 413 no longer occurs
  • the previous Geel low-threshold persisted YOLO/QA 504 no longer occurs
  • Geel threshold=0.05 completed with 2,612 detections, 5,091 raw candidates, 2,479 suppressed duplicates, precision 0.06316998468606431, recall 0.165 and F1 0.09136212624584718
• Full AOI1024 positive persisted QA matrix:
  • Geel best F1: 0.09136212624584718 at threshold 0.05
  • Turnhout best F1: 0.058721074894252295 at threshold 0.05
  • Retie best F1: 0.15621436716077539 at threshold 0.15
  • Westerlo best F1: 0.28703703703703703 at threshold 0.15
• Full AOI1024 hard-negative/background matrix:
  • pure empty Postel/Lommel/Arendonk samples stayed at or near zero detections
  • mixed background candidates still produced false-positive pressure: max detections were 59 at threshold 0.25, 107 at 0.15 and 226 at 0.05
• Fixed scripts/build_detection_model_promotion_report.py after discovering it accepted multi_sample_quality_summary.json but counted positive samples as 0. The report now supports both samples[].runs[] portfolios and items[] multi-sample summaries.
• AOI1024 promotion report path: artifacts/detection-model-promotion/aoi1024visible025e50-full/detection_model_promotion_report.md; recommendation remains none.
• Corrected AOI1024 promotion decision after parser fix:
  • threshold 0.05: 4 positive samples, 9 background samples, mean F1 0.13307746028311157, max background detections 226
  • threshold 0.15: 4 positive samples, 9 background samples, mean F1 0.13900227809255514, max background detections 107
  • threshold 0.25: 4 positive samples, 9 background samples, mean F1 0.09694707724016788, max background detections 59

Known limitations

• The AOI1024 tile audit is still needs_attention: median normalized box area is below gate and small-box share remains high.
• Several dense 1024 GRB reference exports reached the current 1000-feature source cap. Treat those samples as useful but potentially reference-capped until the provider query path supports paging or smaller dense AOIs are chosen.
• The trained model is intentionally inactive. It needs persisted detection QA/QC matrix evidence and background/hard-negative evidence before default promotion.
• The first completed persisted Geel QA run confirms the candidate is not promotion-ready: recall improves at low threshold, but false-positive pressure is too high.
• The complete positive/background evidence confirms the candidate is not promotion-ready. The bottleneck is label/source quality and sample design, not runtime configuration.

Next recommended pass

• Fix dense GRB reference completeness first: add provider-side paging or split dense AOIs so reference exports do not cap at 1000 features, then regenerate AOI1024 labels before another training run.

Sprint 153 - AOI1024 clean-label YOLOv8s candidate gate

What changed

• Built and audited AOI1024 label-quality candidate exports on Tower after the paged GRB reference regeneration:
  • baseline yolo-building-aoi1024-visible025: 144 tiles, 117 positive, 27 negative, 29,170 labels, audit needs_attention, median normalized box area 0.000762939453125, small-box share 0.34744600617072335.
  • yolo-building-aoi1024-visible050: 144 tiles, 117 positive, 27 negative, 28,552 labels, audit needs_attention, small-box share 0.34463435135892406.
  • yolo-building-aoi1024-visible025-tile640: 45,529 labels, audit needs_attention, small-box share 0.4952667530584902.
  • yolo-building-aoi1024-visible025-tile384: 400 tiles, 311 positive, 89 negative, 46,423 labels, audit ok, small-box share 0.215281218361588.
  • yolo-building-aoi1024-visible050-minpx8: 144 tiles, 117 positive, 27 negative, 21,530 labels, audit ok, median normalized box area 0.0010299684375, small-box share 0.14681839294008361.
• Selected /app/storage/operator-data/yolo-building-aoi1024-visible050-minpx8 for the next controlled candidate because it passes the label audit while preserving the 512px runtime scale.
• Trained Tower-local inactive model asset:
  • model path: /app/models/geointel-building-yolov8s-aoi1024clean512e50.pt
  • asset id: geointel-building-yolov8s-aoi1024clean512e50-pt
  • SHA256: 7cfadb684dd56623d2e35ebd65593211d3051908c87121bef438b231d3e47cce
  • training summary: /app/storage/training/operator-yolo/geointel-building-yolov8s-aoi1024clean512e50/training_summary.json
  • base model: /app/models/yolov8s.pt
  • epochs: 50
  • image size: 512
  • batch size: 2
  • device: CPU
  • final Ultralytics validation on best.pt: precision 0.441, recall 0.380, mAP50 0.265, mAP50-95 0.096.
• Verified the live model catalog reports the candidate as status=available, active=false and will_download_models=false.

What was tested

• Tower training command:
  • docker exec -e OPERATOR_YOLO_DATASET_DIR=/app/storage/operator-data/yolo-building-aoi1024-visible050-minpx8 -e YOLO_BASE_MODEL_PATH=/app/models/yolov8s.pt -e TRAIN_OUTPUT_DIR=/app/storage/training/operator-yolo -e TRAIN_RUN_NAME=geointel-building-yolov8s-aoi1024clean512e50 -e TRAIN_MODEL_OUTPUT_PATH=/app/models/geointel-building-yolov8s-aoi1024clean512e50.pt -e TRAIN_EPOCHS=50 -e TRAIN_IMGSZ=512 -e TRAIN_BATCH=2 -e TRAIN_WORKERS=0 -e TRAIN_DEVICE=cpu geointel bash /app/scripts/train_operator_yolo_detector.sh
• Full seven-reference AOI1024 persisted positive QA matrix:
  • command used scripts/run_multi_sample_detection_quality_matrix.sh with manifest /mnt/user/appdata/geointel/storage/operator-data/operator-samples-1024/operator_samples_manifest.json, samples geel mol turnhout herentals balen retie westerlo, model geointel-building-yolov8s-aoi1024clean512e50-pt, tile 512, overlap 64, thresholds 0.25 0.15 0.05 and IoU threshold 0.25.
  • output: /mnt/user/appdata/geointel/artifacts/detection-quality-matrix/multi-sample/aoi1024clean512e50-full/multi_sample_quality_summary.json
  • threshold 0.05: 7 positive samples, mean F1 0.4723513253430784, mean precision 0.44861169484563357, mean recall 0.5039340859731523, min F1 0.4442082890541977.
  • threshold 0.15: 7 positive samples, mean F1 0.4753322215541376, mean precision 0.6673270868402211, mean recall 0.37207511021815726, min F1 0.4055555555555556.
  • threshold 0.25: 7 positive samples, mean F1 0.31021575247724875, mean precision 0.8163316419273472, mean recall 0.19608909809008596, min F1 0.16430903155603915.
• Full nine-sample hard-negative/background matrix:
  • command used scripts/run_operator_hard_negative_detection_matrix.sh with the same AOI1024 manifest, model geointel-building-yolov8s-aoi1024clean512e50-pt, tile 512, overlap 64 and thresholds 0.25 0.15 0.05.
  • output: /mnt/user/appdata/geointel/artifacts/detection-hard-negatives/aoi1024clean512e50-full/hard_negative_matrix_summary.json
  • threshold 0.05: total background detections 488, max sample detections 137.
  • threshold 0.15: total background detections 269, max sample detections 75.
  • threshold 0.25: total background detections 205, max sample detections 55.
• Promotion report:
  • command used scripts/build_detection_model_promotion_report.py with --min-positive-samples 7, --min-background-samples 9, --min-mean-f1 0.25 and --max-background-detections-per-sample 0.
  • output: /mnt/user/appdata/geointel/artifacts/detection-model-promotion/aoi1024clean512e50-full/detection_model_promotion_report.md
  • recommendation: none.

Known limitations

• The clean-label model is materially stronger on positive reference AOIs than the previous AOI1024-visible025 candidate, but it is still not safe as a V1 default because hard-negative/background false positives remain high in mixed wooded AOIs.
• The likely bottleneck has shifted from label-file integrity to discriminating buildings from visually similar background structures and wooded-edge artifacts.
• The model remains inactive. No default model, API contract, migration, provider fetching or download behavior changed.

Next recommended pass

• Build a background-aware training pass instead of another positive-only clean-label pass: combine the visible050-minpx8 positives with stronger unique hard-negative/background retention, then re-run the same seven-positive/nine-background promotion gate before any activation.

Sprint 154 - Background-aware AOI1024 YOLOv8s candidate gate

What changed

• Built a background-aware AOI1024 tile dataset on Tower from the clean positive labels plus explicit background retention:
  • dataset path: /app/storage/operator-data/yolo-building-aoi1024-bgaware512r3
  • audit path: /mnt/user/appdata/geointel/storage/operator-data/yolo-building-aoi1024-bgaware512r3-audit/operator_yolo_dataset_quality_audit.json
  • tile count: 162
  • positive tiles: 117
  • negative tiles: 45
  • label count: 21530
  • background negative repeat: 3
  • audit status: ok
  • warnings: []
• Trained Tower-local inactive model asset:
  • model path: /app/models/geointel-building-yolov8s-aoi1024bg512r3e50.pt
  • asset id: geointel-building-yolov8s-aoi1024bg512r3e50-pt
  • SHA256: e0980572aac90e7efc514608eb16d7de5bfbf27a4bbec04e7bc1bc8c02f9601f
  • training summary: /app/storage/training/operator-yolo/geointel-building-yolov8s-aoi1024bg512r3e50/training_summary.json
  • base model: /app/models/yolov8s.pt
  • epochs: 50
  • image size: 512
  • batch size: 2
  • device: CPU
  • final Ultralytics validation on best.pt: precision 0.440, recall 0.362, mAP50 0.251, mAP50-95 0.0878.
• Verified the live model catalog reports the candidate as status=available, active=false and will_download_models=false.
• No API contract, migration, provider fetching, fake detection data, model download behavior or active model default changed.

What was tested

• Tower training command:
  • docker exec -e OPERATOR_YOLO_DATASET_DIR=/app/storage/operator-data/yolo-building-aoi1024-bgaware512r3 -e YOLO_BASE_MODEL_PATH=/app/models/yolov8s.pt -e TRAIN_OUTPUT_DIR=/app/storage/training/operator-yolo -e TRAIN_RUN_NAME=geointel-building-yolov8s-aoi1024bg512r3e50 -e TRAIN_MODEL_OUTPUT_PATH=/app/models/geointel-building-yolov8s-aoi1024bg512r3e50.pt -e TRAIN_EPOCHS=50 -e TRAIN_IMGSZ=512 -e TRAIN_BATCH=2 -e TRAIN_WORKERS=0 -e TRAIN_DEVICE=cpu geointel bash /app/scripts/train_operator_yolo_detector.sh
• Full seven-reference AOI1024 persisted positive QA matrix:
  • command used scripts/run_multi_sample_detection_quality_matrix.sh with manifest /mnt/user/appdata/geointel/storage/operator-data/operator-samples-1024/operator_samples_manifest.json, samples geel mol turnhout herentals balen retie westerlo, model geointel-building-yolov8s-aoi1024bg512r3e50-pt, tile 512, overlap 64, thresholds 0.25 0.15 0.05 and IoU threshold 0.25.
  • output: /mnt/user/appdata/geointel/artifacts/detection-quality-matrix/multi-sample/aoi1024bg512r3e50-full/multi_sample_quality_summary.json
  • threshold 0.05: 7 positive samples, mean F1 0.4908049127242224, mean precision 0.468604, mean recall 0.520016, total detections 15277.
  • threshold 0.15: 7 positive samples, mean F1 0.5074022485589402, mean precision 0.636639, mean recall 0.424258, total detections 9186.
  • threshold 0.25: 7 positive samples, mean F1 0.44378879337957716, mean precision 0.762683, mean recall 0.316314, total detections 5584.
• Conservative seven-reference AOI1024 persisted positive QA matrix:
  • command used the same manifest/model/tile/overlap with thresholds 0.35 0.45 0.60.
  • output: /mnt/user/appdata/geointel/artifacts/detection-quality-matrix/multi-sample/aoi1024bg512r3e50-high-threshold/multi_sample_quality_summary.json
  • threshold 0.35: 7 positive samples, mean F1 0.32086574003576274, mean precision 0.840006, mean recall 0.202135, total detections 3106.
  • threshold 0.45: 7 positive samples, mean F1 0.20093433626447627, mean precision 0.885043, mean recall 0.116348, total detections 1592.
  • threshold 0.60: 7 positive samples, mean F1 0.06640476239173489, mean precision 0.871611, mean recall 0.035083, total detections 418.
• Full nine-sample hard-negative/background matrix:
  • command used scripts/run_operator_hard_negative_detection_matrix.sh with the AOI1024 manifest, model geointel-building-yolov8s-aoi1024bg512r3e50-pt, tile 512, overlap 64 and thresholds 0.25 0.15 0.05.
  • output: /mnt/user/appdata/geointel/artifacts/detection-hard-negatives/aoi1024bg512r3e50-full/hard_negative_matrix_summary.json
  • threshold 0.05: total background detections 638, max sample detections 184.
  • threshold 0.15: total background detections 390, max sample detections 103.
  • threshold 0.25: total background detections 278, max sample detections 75.
• Conservative nine-sample hard-negative/background matrix:
  • output: /mnt/user/appdata/geointel/artifacts/detection-hard-negatives/aoi1024bg512r3e50-high-threshold/hard_negative_matrix_summary.json
  • threshold 0.35: total background detections 198, max sample detections 55.
  • threshold 0.45: total background detections 148, max sample detections 38.
  • threshold 0.60: total background detections 76, max sample detections 18.
• Promotion reports:
  • balanced thresholds report: /mnt/user/appdata/geointel/artifacts/detection-model-promotion/aoi1024bg512r3e50-full/detection_model_promotion_report.md
  • conservative thresholds report: /mnt/user/appdata/geointel/artifacts/detection-model-promotion/aoi1024bg512r3e50-high-threshold/detection_model_promotion_report.md
  • recommendation: none.

Known limitations

• The background-aware model is the strongest positive-AOI candidate so far and is usable as an explicit review/demo candidate, especially at threshold 0.15 for balance or 0.35 for conservative high-precision review.
• It is still not safe as a V1 default because the full background-candidate gate fails background_false_positive_pressure.
• Some background_candidate AOIs contain GRB reference buildings and should be split into pure-empty negatives versus sparse-positive contextual samples before the next gate; otherwise the hard-negative score remains intentionally conservative but not perfectly diagnostic.
• The model remains inactive. Operators can select it explicitly from the local model asset catalog.

Next recommended pass

• Build a V1 operator detection profile layer in the UI/docs: expose balanced (threshold=0.15) and conservative review (threshold=0.35) as explicit choices for local model assets, with clear warning that the model is not a default-approved detector.
• Clean the background corpus classification: separate pure-empty AOIs from sparse-building contextual AOIs, then retrain or recalibrate against that cleaner gate.

Sprint 155 - Detection operator profiles

What changed

• Added frontend/src/components/detection/detectionProfiles.ts with explicit operator profiles for the inactive geointel-building-yolov8s-aoi1024bg512r3e50-pt local model asset.
• Exposed two deliberate Detection Lab actions:
  • balanced-review: confidence threshold 0.15, positive-AOI F1 0.5074022485589402, precision 0.636639, recall 0.424258, max background detections 103.
  • conservative-review: confidence threshold 0.35, positive-AOI F1 0.32086574003576274, precision 0.840006, recall 0.202135, max background detections 55.
• Applying a profile selects yolo-configured, the local model asset id and the profile threshold. It does not auto-select assets on model catalog load and does not promote the candidate as a default detector.
• Detection Lab now marks both profiles as Candidate only - not default-approved because the promotion recommendation remains none.
• Updated frontend, AI pipeline and TODO documentation.

What was tested

• Added regression coverage in backend/tests/test_sprint155_detection_operator_profiles.py.
• Ran python -m pytest tests/test_sprint155_detection_operator_profiles.py tests/test_sprint122_model_asset_activation_guardrails.py -q.
• Ran python -m pytest in backend: 435 passed.
• Ran python -m compileall backend/app.
• Ran cd frontend && npm run typecheck.
• Ran cd frontend && npm run build.
• Ran bash scripts/run_readiness_check.sh.
• Ran cd backend && python -m alembic heads and cd backend && python -m alembic upgrade head --sql.
• Ran bash -n scripts/live_migration_smoke.sh.

Known limitations

• The profiles are review/demo aids only. The background corpus still needs to be split into pure-empty negatives and sparse-building contextual AOIs before retraining or recalibrating for a default detector decision.
• No backend API contract, migration, provider fetching, fake detection output, model download behavior or active runtime default changed.

Sprint 156 - Background corpus classification

What changed

• Added explicit background category classification to operator sample preparation:
  • pure_empty_negative when a background candidate has zero GRB reference buildings.
  • sparse_building_context when a background candidate has one or more GRB reference buildings.
  • reference_aoi for normal positive reference samples.
• Persisted background_category into generated operator sample manifests and reference GeoJSON metadata.
• Added OPERATOR_BACKGROUND_CATEGORIES to scripts/run_operator_hard_negative_detection_matrix.sh so the strict default-promotion hard-negative gate can run only on pure_empty_negative samples, while sparse_building_context samples can be reviewed separately.
• Preserved background_category in YOLO tile export metadata so negative-tile provenance survives training dataset audits.
• Updated operator pipeline docs, TODO and changelog.

What was tested

• Added regression coverage in backend/tests/test_sprint156_background_corpus_classification.py.
• Ran python -m pytest tests/test_sprint156_background_corpus_classification.py -q.
• Ran python -m pytest tests/test_sprint156_background_corpus_classification.py tests/test_sprint131_operator_sample_expansion.py tests/test_sprint132_operator_hard_negative_matrix.py tests/test_sprint130_operator_yolo_tile_dataset.py -q: 17 passed.
• Ran python -m compileall backend/app.
• Ran python -m pytest in backend: 439 passed.
• Ran cd frontend && npm run typecheck.
• Ran cd frontend && npm run build.
• Ran bash scripts/run_readiness_check.sh.
• Ran cd backend && python -m alembic heads and cd backend && python -m alembic upgrade head --sql.
• Ran bash -n scripts/live_migration_smoke.sh and bash -n scripts/run_operator_hard_negative_detection_matrix.sh.

Known limitations

• This pass adds the cleaner corpus/gate contract only. It does not regenerate Tower manifests, retrain YOLO, rerun the live hard-negative matrices or change any model default.
• No backend API contract, database migration, provider fetching, fake detection output or model download behavior changed.

Next recommended pass

• Redeploy/rebuild the runtime scripts, regenerate the operator sample manifest, then run:
  • OPERATOR_BACKGROUND_CATEGORIES="pure_empty_negative" for the strict default-promotion false-positive gate.
  • OPERATOR_BACKGROUND_CATEGORIES="sparse_building_context" for contextual review evidence.
• Retrain or recalibrate the inactive AOI1024 local model candidate only after those two matrices are available.

Sprint 157 - Background split matrix runner

What changed

• Added scripts/run_background_corpus_split_matrix.sh as the operator wrapper for the next Tower run.
• The wrapper runs scripts/run_operator_hard_negative_detection_matrix.sh twice:
  • OPERATOR_BACKGROUND_CATEGORIES="pure_empty_negative" for the strict default-promotion false-positive gate.
  • OPERATOR_BACKGROUND_CATEGORIES="sparse_building_context" for review-only contextual evidence.
• Added scripts/build_background_corpus_split_report.py to combine both summaries into:
  • background_corpus_split_summary.json
  • background_corpus_split_summary.md
• The combined report records strict_default_gate, context_review, passes_zero_detection_gate, max detection counts and the recommended next step.
• Added readiness coverage for the new Python and Bash scripts.
• Updated operator pipeline docs, TODO and changelog.

What was tested

• Added regression coverage in backend/tests/test_sprint157_background_split_matrix_runner.py.
• Ran python -m pytest tests/test_sprint157_background_split_matrix_runner.py -q.
• Ran python -m pytest tests/test_sprint157_background_split_matrix_runner.py tests/test_sprint156_background_corpus_classification.py tests/test_sprint132_operator_hard_negative_matrix.py -q: 9 passed.
• Ran python -m py_compile scripts/build_background_corpus_split_report.py.
• Ran bash -n scripts/run_background_corpus_split_matrix.sh.
• Ran python -m compileall backend/app.
• Ran python -m pytest in backend: 443 passed.
• Ran cd frontend && npm run typecheck.
• Ran cd frontend && npm run build.
• Ran bash scripts/run_readiness_check.sh.
• Ran cd backend && python -m alembic heads and cd backend && python -m alembic upgrade head --sql.
• Ran bash -n scripts/live_migration_smoke.sh and bash -n scripts/run_background_corpus_split_matrix.sh.

Known limitations

• This pass adds orchestration/report tooling only. It does not run live inference on Tower, retrain YOLO, rerun the split matrices or change any model default.
• No backend API contract, database migration, provider fetching, fake detection output or model download behavior changed.

Next recommended pass

• Rebuild/redeploy the runtime, regenerate the operator manifest if needed, then run scripts/run_background_corpus_split_matrix.sh against http://192.168.10.150:1202.
• Use the emitted split report to decide whether to retrain, recalibrate thresholds or keep the AOI1024 candidate operator-only.

Sprint 158 - Split-aware promotion report

What changed

• Added --background-split-summary support to scripts/build_detection_model_promotion_report.py.
• The promotion report now resolves a split report's pure_empty_negative source summary as the strict default-promotion background gate.
• The same report records sparse_building_context as review-only evidence, including source path and detection-pressure context, without counting it as a default-promotion blocker.
• Kept direct --hard-negative-summary support unchanged for older operator workflows.
• Updated operator pipeline docs, TODO and changelog.

What was tested

• Red step: python -m pytest tests/test_sprint158_promotion_report_split_background.py -q failed because the promotion report required --hard-negative-summary and did not yet accept --background-split-summary.
• Ran python -m pytest tests/test_sprint158_promotion_report_split_background.py -q: 1 passed.
• Ran python -m pytest tests/test_sprint143_detection_model_promotion_report.py tests/test_sprint157_background_split_matrix_runner.py -q: 7 passed.
• Ran python -m py_compile scripts/build_detection_model_promotion_report.py scripts/build_background_corpus_split_report.py.
• Ran python -m pytest tests/test_sprint158_promotion_report_split_background.py tests/test_sprint143_detection_model_promotion_report.py tests/test_sprint157_background_split_matrix_runner.py -q: 8 passed.
• Ran python -m compileall backend/app.
• Ran python -m pytest in backend: 444 passed, 17 existing Pydantic namespace warnings.
• Ran cd frontend && npm run typecheck.
• Ran cd frontend && npm run build.
• Ran bash scripts/run_readiness_check.sh: passed.
• Ran cd backend && python -m alembic heads: 202606120900 (head).
• Ran cd backend && python -m alembic upgrade head --sql.
• Ran bash -n scripts/live_migration_smoke.sh.

Known limitations

• This pass is report/tooling only. It does not run live split matrices on Tower, retrain YOLO, change a model default, change API contracts, change migrations, fetch providers, download model weights or create fake detections.
• The AOI1024 background-aware local model remains explicit operator-review only until the split matrices plus positive-AOI QA/QC evidence pass the documented gates.

Next recommended pass

• After redeploy, run scripts/run_background_corpus_split_matrix.sh on Tower for geointel-building-yolov8s-aoi1024bg512r3e50-pt.
• Feed the generated background_corpus_split_summary.json into scripts/build_detection_model_promotion_report.py --background-split-summary together with the seven-AOI positive summary.
• If pure-empty false-positive pressure still fails, retrain or recalibrate before any default activation. If pure-empty passes, inspect sparse-context review evidence before deciding whether to keep the model operator-only or prepare a guarded default-candidate decision.

Sprint 159 - Split-background promotion workflow wrapper

What changed

• Added scripts/run_split_background_promotion_workflow.sh.
• The wrapper runs scripts/run_background_corpus_split_matrix.sh, verifies the emitted background_corpus_split_summary.json, then calls scripts/build_detection_model_promotion_report.py --background-split-summary.
• The wrapper requires PROMOTION_POSITIVE_PORTFOLIO_PATH and exposes promotion gate overrides through environment variables.
• Added readiness syntax coverage for the new wrapper.
• Updated operator pipeline docs, TODO and changelog.

What was tested

• Red step: python -m pytest tests/test_sprint159_split_promotion_workflow.py -q failed because the wrapper script and readiness syntax check did not exist.
• Ran python -m pytest tests/test_sprint159_split_promotion_workflow.py -q: 2 passed.
• Ran bash -n scripts/run_split_background_promotion_workflow.sh.
• Ran python -m pytest tests/test_sprint159_split_promotion_workflow.py tests/test_sprint158_promotion_report_split_background.py tests/test_sprint157_background_split_matrix_runner.py -q: 7 passed.
• Ran bash -n scripts/run_readiness_check.sh.
• Ran python -m compileall backend/app.
• Ran python -m pytest in backend: 446 passed, 17 existing Pydantic namespace warnings.
• Ran cd frontend && npm run typecheck.
• Ran cd frontend && npm run build.
• Ran cd backend && python -m alembic heads: 202606120900 (head).
• Ran cd backend && python -m alembic upgrade head --sql.
• Ran bash -n scripts/live_migration_smoke.sh.
• Ran bash scripts/run_readiness_check.sh: passed.

Known limitations

• This pass adds operator orchestration only. It does not run live inference locally, retrain YOLO, activate a model default, change backend API contracts, change migrations, fetch providers, fetch weights or create fake detections.
• http://192.168.10.150:1202 is reachable from this workstation, but ssh -p 222 root@192.168.10.150 returned Permission denied (publickey). Port 222 appears to be the Gitea SSH endpoint rather than an Unraid shell endpoint, so Docker rebuild/restart still needs the existing Unraid deployment path or a separate shell-access route.

Next recommended pass

• Redeploy/pull commit on Tower through the existing Gitea/Unraid flow.
• Run:
  • PROMOTION_POSITIVE_PORTFOLIO_PATH=... bash scripts/run_split_background_promotion_workflow.sh http://192.168.10.150:1202
• Review the generated split-aware promotion report before any retraining, recalibration or default-model decision.

Sprint 160 - Split-background promotion preflight

What changed

• Added --preflight-only to scripts/run_split_background_promotion_workflow.sh.
• Preflight now checks:
  • PROMOTION_POSITIVE_PORTFOLIO_PATH exists and is readable.
  • OPERATOR_SAMPLE_MANIFEST_PATH exists and is readable.
  • the manifest contains both pure_empty_negative and sparse_building_context background categories.
  • Python and curl are available.
  • the runtime frontend API proxy returns the canonical data envelope from /api/v1/projects.
• Updated operator docs, TODO and changelog with the quick post-redeploy preflight command.

What was tested

• Red step: python -m pytest tests/test_sprint159_split_promotion_workflow.py -q failed because --preflight-only was not yet present.
• Ran python -m pytest tests/test_sprint159_split_promotion_workflow.py -q: 3 passed.
• Ran bash -n scripts/run_split_background_promotion_workflow.sh.
• Ran python -m pytest tests/test_sprint159_split_promotion_workflow.py tests/test_sprint158_promotion_report_split_background.py -q: 4 passed.
• Ran curl.exe -fsS http://192.168.10.150:1202/api/v1/projects: runtime API proxy returned a canonical data envelope.
• Ran python -m compileall backend/app.
• Ran python -m pytest in backend: 447 passed, 17 existing Pydantic namespace warnings.
• Ran cd frontend && npm run typecheck.
• Ran cd frontend && npm run build.
• Ran cd backend && python -m alembic heads: 202606120900 (head).
• Ran cd backend && python -m alembic upgrade head --sql.
• Ran bash -n scripts/live_migration_smoke.sh.
• Ran bash scripts/run_readiness_check.sh: passed.

Known limitations

• Preflight is intentionally non-mutating and does not run inference, create datasets, retrain models, activate defaults, fetch providers, fetch weights or change API/database behavior.
• Full split-background matrix execution still requires redeploying this commit to the Tower/Unraid runtime and running the wrapper in the runtime checkout where the positive portfolio and operator manifest artifacts exist.

Next recommended pass

• Redeploy/pull on Tower, then run:
  • PROMOTION_POSITIVE_PORTFOLIO_PATH=... OPERATOR_SAMPLE_MANIFEST_PATH=... bash scripts/run_split_background_promotion_workflow.sh --preflight-only http://192.168.10.150:1202
• If preflight passes, rerun without --preflight-only to generate the split summary and split-aware promotion report.

Sprint 161 - Widescreen workbench support

What changed

• Added dedicated frontend CSS breakpoints for 1800px and 2200px workbench widths.
• Wide screens now expand the workbench shell columns, inspector padding, Data/Analysis/AI/Export grids and Map workspace controls without changing workflows.
• Ultrawide screens prioritize the MapLibre review frame with a taller map and wider toolbar/inspection layout.
• Added static regression coverage for widescreen/ultrawide layout contracts.
• Updated frontend README, TODO and changelog.

What was tested

• Red step: python -m pytest tests/test_sprint161_widescreen_workbench.py -q failed before the widescreen CSS contracts existed.
• Ran python -m pytest tests/test_sprint161_widescreen_workbench.py tests/test_sprint53_selection_ergonomics.py tests/test_sprint85_map_workspace_density.py tests/test_sprint113_calm_workbench_layout.py -q: 13 passed.
• Ran python -m compileall backend/app.
• Ran python -m pytest in backend: 449 passed, 17 existing Pydantic protected-namespace warnings.
• Ran npm run typecheck in frontend.
• Ran npm run build in frontend.
• Ran python -m alembic heads in backend: 202606120900 (head).
• Ran python -m alembic upgrade head --sql in backend.
• Ran bash -n scripts/live_migration_smoke.sh.
• Ran bash scripts/run_readiness_check.sh: passed.
• Ran a local Vite preview visual/layout check at 2560x1080 through the in-app browser. The workbench measured no horizontal overflow, shell columns 240px 1889px 416px, and the Map workspace rendered an 832px high MapLibre frame with the road basemap visible.

Known limitations

• Local docker compose config could not be run on this Windows workstation because the Docker CLI is not installed or not on PATH.
• The live Tower/Unraid runtime at http://192.168.10.150:1202 will show the widescreen changes only after redeploying this commit.
• The local preview showed an expected API error because it served the built frontend without the Docker backend proxy; the layout and MapLibre basemap rendered correctly.

Next recommended pass

• Redeploy/pull this commit on Tower/Unraid and visually recheck http://192.168.10.150:1202 on the actual widescreen monitor.
• Continue with the split-background promotion preflight/matrix flow once the runtime has been rebuilt.

Sprint 161 - Tower redeploy and live widescreen runtime verification

What changed

• Redeployed Tower/Unraid from commit 07ea8db with the existing all-in-one deploy path.
• Preserved the remote AI runtime setting by deploying with GEOINTEL_INSTALL_AI=true.
• The deployed geointel container is healthy and published on 0.0.0.0:1202->80/tcp.
• No application code, API contract, migration, provider fetching, model default, model download behavior or fake data path changed in this pass.

What was tested

• Verified Tower SSH access through root@192.168.10.150 with the widefrog_unraid_deploy key.
• Verified the remote checkout reset to 07ea8db.
• Ran the Tower deploy script with AI dependencies enabled.
• Deploy build produced frontend asset assets/index-CxHabIo7.css; the live CSS contains the Sprint 161 wide/ultrawide layout rules including 15rem minmax(0,1fr) 26rem and min-height:42rem.
• Live migration smoke passed inside the geointel container:
  • database connection ok
  • PostGIS 3.6
  • database collation ok
  • required runtime schema objects ok
  • Alembic head 202606120900
• Browser runtime smoke passed against http://192.168.10.150:1202.
• GIS runtime smoke passed against http://192.168.10.150:1202.
• Health endpoint returned status=ok, database=ok.
• YOLO preflight returned:
  • dependencies_available=true
  • model_file_exists=true
  • torch_version=2.13.0
  • ultralytics_version=8.4.92
  • will_download_models=false
  • will_run_inference=false without a tile manifest

Known limitations

• The optional local screenshot script could not run in this Windows runner because Node cannot import Playwright here. The script remains optional and reports the documented install commands.
• The in-app browser could not attach a fresh browser-use tab during this pass, so the final visual confirmation is based on deploy smoke, live CSS verification and runtime API/GIS/AI checks rather than a new inline screenshot.

Next recommended pass

• Manually refresh the already-open http://192.168.10.150:1202 browser tab on the actual widescreen monitor to observe the new layout.
• Continue with the split-background promotion preflight/matrix workflow now that Tower is rebuilt with the latest frontend and AI runtime.

Sprint 162 - Split-background promotion runtime pass

What changed

• Hardened scripts/run_split_background_promotion_workflow.sh preflight so legacy operator manifests without explicit background_category are handled consistently with run_operator_hard_negative_detection_matrix.sh.
• Missing background categories are now derived from reference_feature_count: 0 becomes pure_empty_negative, and background samples with references become sparse_building_context.
• Added a regression test for the derived-category preflight path.
• Updated operator docs and changelog. No API contract, migration, provider fetching, model default, fake output or model download behavior changed.

What was tested locally

• Ran python -m pytest tests/test_sprint159_split_promotion_workflow.py -q: 4 passed.
• Ran python -m pytest tests/test_sprint157_background_split_matrix_runner.py tests/test_sprint158_promotion_report_split_background.py tests/test_sprint159_split_promotion_workflow.py -q: 9 passed.
• Ran bash -n scripts/run_split_background_promotion_workflow.sh.
• Ran python -m compileall backend/app.
• Ran python -m pytest in backend: 450 passed, 17 existing Pydantic protected-namespace warnings.
• Ran npm run typecheck in frontend.
• Ran npm run build in frontend.
• Ran bash scripts/run_readiness_check.sh: passed.

Tower runtime evidence

• Pushed commit a499c5f and fast-forwarded the Tower checkout.
• Ran split-background preflight against http://192.168.10.150:1202 with:
  • PROMOTION_POSITIVE_PORTFOLIO_PATH=artifacts/detection-quality-matrix/multi-sample/aoi1024bg512r3e50-full/multi_sample_quality_summary.json
  • OPERATOR_SAMPLE_MANIFEST_PATH=storage/operator-data/operator-samples-1024/operator_samples_manifest.json
  • result: passed.
• Ran the full low/mid-threshold split workflow into:
  • background split: artifacts/detection-hard-negatives/background-split/aoi1024bg512r3e50-split-20260710T222641Z
  • promotion report: artifacts/detection-model-promotion/split-aware/aoi1024bg512r3e50-split-20260710T222641Z/detection_model_promotion_report.json
  • result: no recommended candidate because 0.15 produced 46 pure-empty detections on postel_bos, while 0.35 had no matching positive evidence in that portfolio.
• Ran high-threshold preflight and full split workflow using:
  • PROMOTION_POSITIVE_PORTFOLIO_PATH=artifacts/detection-quality-matrix/multi-sample/aoi1024bg512r3e50-high-threshold/multi_sample_quality_summary.json
  • QUALITY_THRESHOLDS="0.35 0.45 0.6"
  • background split: artifacts/detection-hard-negatives/background-split/aoi1024bg512r3e50-high-threshold-split-20260710T222934Z
  • promotion report: artifacts/detection-model-promotion/split-aware/aoi1024bg512r3e50-high-threshold-split-20260710T222934Z/detection_model_promotion_report.json
• High-threshold strict pure-empty gate passed:
  • 3 pure-empty samples: arendonk_heide, lommel_heide, postel_bos
  • 9 pure-empty runs
  • total detections: 0
  • max detections per sample/threshold run: 0
• Recommended candidate:
  • geointel-building-yolov8s-aoi1024bg512r3e50-pt|512|64|0.35
  • positive samples: 7
  • pure-empty background samples: 3
  • mean F1: 0.32086574003576274
  • mean precision: 0.8400057773951873
  • mean recall: 0.20213514285308795
  • max pure-empty detections: 0
  • promotion status: promote_candidate
• Sparse-context review remained review-only:
  • 6 sparse-context samples
  • 18 sparse-context runs
  • total detections: 422
  • max sparse-context detections: 55

Known limitations

• The recommended candidate has conservative recall (0.2021) at threshold 0.35; it is suitable as an operator-review candidate, not as an automatically activated production default.
• The model default was not changed. Activation should remain a separate explicit operator decision after reviewing the high-threshold report.

Next recommended pass

• Add a guarded model activation/operator-selection workflow that can mark a promoted candidate as active only after the report artifact and candidate key are explicitly supplied.

Sprint 163 - Guarded promoted YOLO activation workflow

What changed

• Added scripts/activate_promoted_yolo_candidate.py.
• The helper validates:
  • the promotion report file exists and is valid JSON;
  • the exact supplied candidate_key matches the report recommended candidate;
  • the candidate has promotion_status=promote_candidate;
  • positive sample count, background sample count, mean F1 and max background detections still satisfy report gates;
  • the candidate model_asset_id resolves to an existing local model file under the mounted models directory.
• The helper emits .env updates in dry-run mode by default and writes them only when --apply is supplied.
• Updated Detection Lab operator profiles:
  • balanced-review at threshold 0.15 remains candidate-only because pure-empty false-positive pressure failed.
  • conservative-review at threshold 0.35 is marked as promoted/default-approved based on the split-background pure-empty gate.
• Added docs for the guarded activation command in docs/AI_PIPELINES.md, scripts/README.md, backend/README.md and frontend/README.md.
• Added readiness coverage for compiling the new helper.
• No API contract, database migration, provider fetching, fake detection path, model file mutation, model download or automatic runtime activation was introduced in code.

What was tested locally

• RED: python -m pytest tests/test_sprint162_promoted_model_activation.py -q failed while scripts/activate_promoted_yolo_candidate.py was absent.
• RED: python -m pytest tests/test_sprint155_detection_operator_profiles.py -q failed before conservative-review was marked promoted.
• RED: python -m pytest tests/test_sprint162_promoted_model_activation.py::test_readiness_gate_compiles_promoted_activation_script -q failed before readiness compiled the helper.
• Ran python -m pytest tests/test_sprint162_promoted_model_activation.py tests/test_sprint155_detection_operator_profiles.py -q: 7 passed.
• Ran python -m py_compile scripts/activate_promoted_yolo_candidate.py.
• Ran python -m compileall backend/app.
• Ran python -m pytest in backend: 454 passed, 17 existing Pydantic protected-namespace warnings.
• Ran npm run typecheck in frontend.
• Ran npm run build in frontend.
• Ran bash scripts/run_readiness_check.sh: passed.

Known limitations

• The helper updates runtime environment only; a container restart or rebuild is still required for YOLO_MODEL_PATH changes to take effect.
• The promoted threshold is represented in the operator profile and promotion report. The backend detection endpoint still requires clients to submit the intended confidence threshold explicitly.

Next recommended pass

• Push this helper to Tower, run it first as dry-run against the high-threshold promotion report, then apply and redeploy/restart only if the emitted YOLO_MODEL_PATH matches the promoted local asset.

Sprint 164 - Live operational YOLO detection and QA smoke

What changed

• No product code, API contract, migration, model artifact, fake-data path or provider-fetching behavior changed.
• Ran the deployed all-in-one Tower runtime at http://192.168.10.150:1202 through a real raster/reference detection and QA workflow using existing operator data and the local promoted YOLO model asset.

Tower runtime evidence

• Runtime URL: http://192.168.10.150:1202
• Raster input: /mnt/user/appdata/geointel/storage/operator-data/operator-samples-1024/geel_orthophoto_wms_1024.tif
• Reference input: /mnt/user/appdata/geointel/storage/operator-data/operator-samples-1024/geel_grb_gbg_buildings.geojson
• Model asset: geointel-building-yolov8s-aoi1024bg512r3e50-pt
• Project: 865746ee-10ce-40a1-a3da-98b2182200e5
• Raster dataset: c87ed009-0bf0-4a34-adc5-51e6747d847b
• Reference dataset: d9bca687-20ca-4609-8c15-d24d240cfae6
• Tile manifest: /app/storage/tiles/865746ee-10ce-40a1-a3da-98b2182200e5/c87ed009-0bf0-4a34-adc5-51e6747d847b/b1335951-4ead-4e08-9c5f-08c67e026a8f/manifest.json
• Analysis run: 45159e0b-36be-4300-8132-fef3a1e6b667
• Persisted detections: 333
• Quality check: 2e696dca-dea6-42d1-af2a-4894b182d427
• Detection export: 339344f7-38e3-4558-b66b-459726051bac

Validation

• Ran scripts/verify_real_data_detection_qa_workflow.sh against the deployed Tower runtime: passed.
• Confirmed detection run status: success.
• Confirmed detection list endpoint returned 333 persisted detections with real source tile provenance.
• Confirmed detection GeoJSON endpoint returned a FeatureCollection with 333 persisted geometry features.
• Confirmed detection export content returned a detection_geojson FeatureCollection with 333 features.
• Confirmed QA/QC persisted metrics:
  • precision: 0.21621621621621623
  • recall: 0.031746031746031744
  • F1: 0.05536332179930796
  • mean IoU: 0.5697275247203281
  • false positives: 261
  • false negatives: 2196
• Confirmed QA evidence overlay endpoint returned a FeatureCollection with 2601 features and no warnings.

Known limitations

• The model is operational and demonstrable, but the Geel smoke confirms low recall at the current conservative threshold. It should remain an operator-review detector, not an automated decision engine.
• Further training data quality work remains required before treating the detector as production-grade.

Next recommended pass

• Add/curate more high-quality positive AOIs and cleaner building labels, then rerun the multi-AOI calibration and promotion gate before changing default operator thresholds.

Sprint 165 - Clean AOI1024 YOLO dataset and promotion rejection

What changed

• Hardened scripts/export_operator_yolo_tile_dataset.py so legacy operator manifests without explicit background_category now derive background categories consistently with the split-background evaluator:
  • reference_feature_count == 0 -> pure_empty_negative
  • reference_feature_count > 0 -> sparse_building_context
  • normal reference samples -> reference_aoi
• Added regression coverage in:
  • backend/tests/test_sprint130_operator_yolo_tile_dataset.py
  • backend/tests/test_sprint156_background_corpus_classification.py
• Documented the clean AOI1024 export/audit profile in scripts/README.md.
• Updated docs/TODO.md with the clean dataset and rejected candidate state.
• No API contract, migration, default model activation, provider fetch path, model download behavior or fake detection path changed.

Local validation

• RED: python -m pytest backend/tests/test_sprint130_operator_yolo_tile_dataset.py::test_background_category_is_derived_for_legacy_operator_manifests -q failed before the exporter helper existed.
• GREEN: same targeted test passed after adding background_category_for_sample.
• Ran python -m pytest backend/tests/test_sprint130_operator_yolo_tile_dataset.py backend/tests/test_sprint146_operator_yolo_dataset_quality_audit.py backend/tests/test_sprint157_background_split_matrix_runner.py -q: 12 passed.
• Ran python -m pytest backend/tests/test_sprint130_operator_yolo_tile_dataset.py backend/tests/test_sprint156_background_corpus_classification.py -q: 11 passed.
• Ran bash scripts/run_readiness_check.sh: 457 backend tests passed, frontend typecheck passed, frontend build passed, readiness passed.

Tower runtime evidence

• Pushed commit b3bd34c and redeployed the all-in-one Tower runtime at http://192.168.10.150:1202.
• Deploy validation passed:
  • live migration smoke passed;
  • browser runtime verification passed;
  • container exposed 0.0.0.0:1202->80/tcp.
• Regenerated clean AOI1024 tile dataset:
  • dataset: /app/storage/operator-data/yolo-building-aoi1024-cleanpx12vis035
  • tile count: 144
  • positive tiles: 114
  • negative tiles: 30
  • labels: 14632
  • min_label_px=12
  • min_label_visible_ratio=0.35
  • background categories in tile summary:
    • pure_empty_negative=27
    • sparse_building_context=54
    • reference_aoi=63
• Dataset audit:
  • report: /app/artifacts/operator-yolo-dataset-audit/aoi1024-cleanpx12vis035/operator_yolo_dataset_quality_audit.json
  • status: ok
  • positive sample count: 13
  • background sample count: 9
  • invalid labels: 0
  • missing label files: 0
  • median normalized box area: 0.001373291015625
  • small-box share: 0.0

Training and evaluation

• Trained a new inactive local model:
  • model asset id: geointel-building-yolov8s-aoi1024cleanpx12vis035e50-pt
  • model file: /app/models/geointel-building-yolov8s-aoi1024cleanpx12vis035e50.pt
  • SHA256: 4863b27717cb7fd3126ccf86a988b3c1863b5fb9271cf35370ca2473b9ed10f4
  • base model: /app/models/yolov8s.pt
  • dataset: /app/storage/operator-data/yolo-building-aoi1024-cleanpx12vis035/dataset.yaml
  • epochs: 50
  • image size: 512
  • batch: 4
  • device: cpu
  • active runtime model: unchanged
• Ran positive multi-AOI matrix:
  • output: artifacts/detection-quality-matrix/multi-sample/aoi1024cleanpx12vis035e50-positive/multi_sample_quality_summary.json
  • samples: geel, mol, turnhout, herentals, balen, retie, westerlo
  • thresholds: 0.35, 0.25, 0.15
• Ran split-background promotion workflow:
  • background split: artifacts/detection-hard-negatives/background-split/aoi1024cleanpx12vis035e50-split/background_corpus_split_summary.json
  • promotion report: artifacts/detection-model-promotion/split-aware/aoi1024cleanpx12vis035e50-split/detection_model_promotion_report.json
  • recommended candidate: none

Promotion result

• geointel-building-yolov8s-aoi1024cleanpx12vis035e50-pt|512|64|0.15
  • rejected: positive_mean_f1_below_gate, background_false_positive_pressure
  • positive samples: 7
  • background samples: 3
  • mean F1: 0.1542209371995884
  • mean precision: 0.18801639524786692
  • mean recall: 0.13146097412721683
  • max pure-empty detections: 2
• geointel-building-yolov8s-aoi1024cleanpx12vis035e50-pt|512|64|0.25
  • rejected: positive_mean_f1_below_gate
  • mean F1: 0.14443976458069788
  • mean precision: 0.24254741138809843
  • mean recall: 0.10383903038875668
  • max pure-empty detections: 0
• geointel-building-yolov8s-aoi1024cleanpx12vis035e50-pt|512|64|0.35
  • rejected: positive_mean_f1_below_gate
  • mean F1: 0.11851668674424971
  • mean precision: 0.30588130541017644
  • mean recall: 0.07481388046665544
  • max pure-empty detections: 0

Known limitations

• The clean label filter fixed the small-box audit problem but did not improve promotion-quality detection metrics.
• The new model remains useful evidence only and must stay inactive.
• The current limiting factor is not script/runtime wiring; it is training data representativeness and label geometry quality for small/dense Belgian building footprints.

Next recommended pass

• Add building-size/visibility diagnostics per AOI and use them to choose or generate better positive samples before another training run. Do not spend more CPU on the current cleanpx dataset without changing the sample/label strategy.

Sprint 166 - Per-sample YOLO dataset audit diagnostics

What changed

• Extended scripts/audit_operator_yolo_dataset_quality.py with per-sample YOLO label diagnostics in sample_summaries:
  • parsed label count;
  • invalid label count;
  • missing label file count;
  • median/mean normalized box area;
  • median normalized width/height;
  • small-box count/share;
  • sample-specific quality warning codes.
• Kept the existing global audit gates unchanged.
• Updated the Markdown audit output so each AOI/sample line includes the new label-quality signals.
• Added regression assertions in backend/tests/test_sprint146_operator_yolo_dataset_quality_audit.py.
• Updated scripts/README.md to document per-sample diagnostics.

Local validation

• RED: python -m pytest backend/tests/test_sprint146_operator_yolo_dataset_quality_audit.py::test_operator_yolo_dataset_quality_audit_reports_dataset_risks -q failed with KeyError: 'parsed_label_count' before sample diagnostics existed.
• GREEN: same targeted test passed after adding sample label stats.
• Ran python -m pytest backend/tests/test_sprint146_operator_yolo_dataset_quality_audit.py backend/tests/test_sprint130_operator_yolo_tile_dataset.py backend/tests/test_sprint156_background_corpus_classification.py -q: 12 passed.
• Ran bash scripts/run_readiness_check.sh: 457 backend tests passed, frontend typecheck passed, frontend build passed, readiness passed.

Tower runtime evidence

• Pushed commit 7ccabf5 and redeployed the all-in-one Tower runtime at http://192.168.10.150:1202.
• Deploy validation passed:
  • live migration smoke passed;
  • browser runtime verification passed;
  • container exposed 0.0.0.0:1202->80/tcp.
• Re-ran the clean AOI1024 audit with sample diagnostics:
  • report: /app/artifacts/operator-yolo-dataset-audit/aoi1024-cleanpx12vis035-sample-diagnostics/operator_yolo_dataset_quality_audit.json
  • status: ok
  • tile count: 144
  • positive tiles: 114
  • negative tiles: 30
  • labels: 14632
  • parsed labels: 14632
  • median normalized box area: 0.001373291016
  • small-box share: 0.0
  • sample count: 16
• All samples reported quality_warnings=[] under the current gates.
• Lowest positive/context label counts:
  • ravels_bos: 22 parsed labels, median box area 0.001140594385
  • geel_bel: 56 parsed labels, median box area 0.001411437891
  • kasterlee_bos: 73 parsed labels, median box area 0.001522064229
  • dessel_heide: 77 parsed labels, median box area 0.001495361328
  • meerhout_bos: 99 parsed labels, median box area 0.001232147197

Known limitations

• Numeric label diagnostics now pass, but the rejected aoi1024cleanpx12vis035e50 model proves that numeric gates alone are insufficient.
• The next unknown is visual alignment and semantic fit: whether GRB building boxes line up well enough with the orthophoto tiles and whether sparse/context AOIs represent the false-positive modes seen during promotion.

Next recommended pass

• Design and add a visual YOLO label QA artifact generator: deterministic contact sheets of selected train/val tiles with YOLO boxes overlaid on imagery, grouped by AOI/sample and label density. Use it before another training run.

Sprint 167 - Operator YOLO visual label QA contact sheets

What changed

• Added scripts/render_operator_yolo_label_qa_contact_sheets.py to render deterministic operator-only contact sheets from existing YOLO tile datasets.
• The script reads the existing yolo_tile_dataset_summary.json, image tiles and label files, then writes:
  • operator_yolo_label_qa_summary.json;
  • operator_yolo_label_qa_contact_sheet.md;
  • one or more PNG contact sheets with YOLO labels drawn over the tile imagery.
• Added explicit checks for missing images, missing label files, invalid YOLO rows and blank-looking/low-variance tiles.
• Added regression coverage in backend/tests/test_sprint167_operator_yolo_label_qa_contact_sheets.py.
• Updated the all-in-one Dockerfile so operator QA scripts are copied after the expensive dependency layer, keeping future script-only rebuilds cache-friendlier.
• Updated Docker runtime tests so the all-in-one image keeps packaging the operator scripts needed on Tower.
• No inference, training, provider fetch, database mutation, model activation or fake detection path was introduced.

Local validation

• RED: python -m pytest backend/tests/test_sprint167_operator_yolo_label_qa_contact_sheets.py -q failed before the contact-sheet script existed.
• GREEN: same targeted test passed after adding the renderer.
• RED: the low-variance regression failed before low_visual_variance_tile_count existed.
• GREEN: same targeted test passed after adding low-variance tile reporting.
• Ran python -m pytest backend/tests/test_docker_runtime_config.py::test_all_in_one_dockerfile_copies_operator_scripts_for_runtime_use backend/tests/test_docker_runtime_config.py::test_all_in_one_dockerfile_copies_operator_scripts_after_dependency_install backend/tests/test_sprint167_operator_yolo_label_qa_contact_sheets.py -q: 3 passed.
• Ran bash scripts/run_readiness_check.sh: 459 backend tests passed, frontend typecheck passed, frontend build passed, readiness passed.

Tower runtime evidence

• Pushed commits fbccf83 and 5688fee, then redeployed the all-in-one Tower runtime at http://192.168.10.150:1202.
• Docker storage had filled during the first build attempt. Cleaned Docker build cache and dangling images only; application volumes and appdata were not pruned. Docker reclaimed 98.38GB.
• Deploy validation for commit 5688fee passed:
  • live migration smoke passed;
  • browser runtime verification passed;
  • container exposed 0.0.0.0:1202->80/tcp;
  • Unraid icon check remained OK.
• Rendered the clean AOI1024 visual label QA artifact inside the live container:
  • summary: /app/artifacts/operator-yolo-label-qa/aoi1024-cleanpx12vis035/operator_yolo_label_qa_summary.json
  • Markdown: /app/artifacts/operator-yolo-label-qa/aoi1024-cleanpx12vis035/operator_yolo_label_qa_contact_sheet.md
  • contact sheet: /app/artifacts/operator-yolo-label-qa/aoi1024-cleanpx12vis035/contact_sheet_001.png
• Runtime summary:
  • status: ok
  • selected tiles: 32
  • rendered tiles: 32
  • valid labels: 7670
  • invalid labels: 0
  • missing images: 0
  • missing label files: 0
  • low-variance tiles: 6
• Visual inspection confirmed:
  • dense positive tiles show yellow YOLO boxes over real orthophoto imagery;
  • the six low-variance tiles are arendonk_heide pure-empty negative validation tiles with no labels and blank-looking imagery.

Known limitations

• The contact sheet now makes visual label inspection possible, but it also proves that the current clean AOI1024 dataset still contains blank-looking pure-empty negative tiles.
• Those blank/low-variance negatives should not be used blindly for the next training run. They can distort the background corpus and do not represent realistic aerial false-positive pressure.
• The current inactive model/promotion state remains unchanged.

Next recommended pass

• Add no-data/low-variance filtering to the operator YOLO tile export path, regenerate the clean AOI1024 dataset, rerun the contact-sheet QA, and only then consider another training attempt.

Sprint 168 - Operator YOLO low-variance negative filtering

What changed

• Added opt-in low-variance negative filtering to scripts/export_operator_yolo_tile_dataset.py.
• Added CLI/env controls:
  • --drop-low-variance-negatives / OPERATOR_YOLO_DROP_LOW_VARIANCE_NEGATIVES;
  • --blank-range-threshold / OPERATOR_YOLO_BLANK_RANGE_THRESHOLD.
• The filter evaluates the rendered raster tile image and skips only negative tiles when enabled.
• Positive/labeled tiles are never removed by this variance gate.
• Kept tile records now include low_visual_variance.
• Skipped blank/no-data negative records use skip_reason="low_visual_variance_negative".
• Dataset summaries now include:
  • drop_low_variance_negatives;
  • blank_range_threshold;
  • skipped_low_variance_negative_tile_count.
• Updated operator documentation with the refreshed AOI1024 cleanpx export command.
• Added design and execution plan docs under docs/superpowers/.

Local validation

• RED: python -m pytest backend/tests/test_sprint130_operator_yolo_tile_dataset.py::test_export_can_skip_low_variance_negative_tiles -q failed because export_sample_tiles() did not accept drop_low_variance_negatives.
• GREEN: same targeted test passed after adding the filter.
• Ran python -m pytest backend/tests/test_sprint130_operator_yolo_tile_dataset.py -q: 8 passed.
• Ran python -m pytest backend/tests/test_sprint130_operator_yolo_tile_dataset.py backend/tests/test_sprint167_operator_yolo_label_qa_contact_sheets.py backend/tests/test_docker_runtime_config.py::test_all_in_one_dockerfile_copies_operator_scripts_for_runtime_use backend/tests/test_docker_runtime_config.py::test_all_in_one_dockerfile_copies_operator_scripts_after_dependency_install -q: 11 passed.
• Ran python scripts/export_operator_yolo_tile_dataset.py --help: the CLI exposes --drop-low-variance-negatives, --no-drop-low-variance-negatives and --blank-range-threshold without loading GIS dependencies.
• Ran bash scripts/run_readiness_check.sh: 460 backend tests passed, frontend typecheck passed, frontend build passed, readiness passed.

Tower runtime evidence

• Pushed commit a159370 and redeployed the all-in-one Tower runtime at http://192.168.10.150:1202.
• Deploy validation passed:
  • live migration smoke passed;
  • browser runtime verification passed;
  • container exposed 0.0.0.0:1202->80/tcp.
• Regenerated the AOI1024 cleanpx dataset with low-variance negative filtering enabled:
  • dataset: /app/storage/operator-data/yolo-building-aoi1024-cleanpx12vis035
  • drop_low_variance_negatives=true
  • blank_range_threshold=3
  • tile count: 135
  • positive tiles: 114
  • negative tiles: 21
  • skipped negative tiles: 9
  • skipped low-variance negative tiles: 9
  • labels: 14632
  • train tiles: 108
  • validation tiles: 27
• Dataset audit:
  • report: /app/artifacts/operator-yolo-dataset-audit/aoi1024-cleanpx12vis035-lowvarfilter/operator_yolo_dataset_quality_audit.json
  • status: ok
  • invalid labels: 0
  • missing label files: 0
  • median normalized box area: 0.001373291016
  • small-box share: 0.0
• Visual label QA:
  • report: /app/artifacts/operator-yolo-label-qa/aoi1024-cleanpx12vis035-lowvarfilter/operator_yolo_label_qa_summary.json
  • contact sheet: /app/artifacts/operator-yolo-label-qa/aoi1024-cleanpx12vis035-lowvarfilter/contact_sheet_001.png
  • selected tiles: 32
  • rendered tiles: 32
  • valid labels: 7670
  • invalid labels: 0
  • missing images: 0
  • missing label files: 0
  • low-variance rendered tiles: 0
• Visual inspection confirmed that the previous blank white arendonk_heide negatives are no longer present in the review sheet. The remaining selected pure-empty negatives are real visible orthophoto/context tiles.

Known limitations

• The low-variance gate is deliberately simple and only identifies visually blank/no-data-looking negative tiles.
• Operator visual contact-sheet review remains required before any new training run.
• The filtered dataset is now a cleaner input candidate, but model training is still not guaranteed to improve QA/QC; another training run must be gated through the existing positive-AOI and background promotion reports.

Next recommended pass

• Train one inactive candidate from the filtered AOI1024 cleanpx dataset, then run the existing positive-AOI matrix and split-background promotion workflow before considering default activation.

Sprint 171 - Positive AOI expansion and split safety

What changed

• Converted the Sprint 170 false-negative evidence into a guarded data action instead of another blind training run.
• Added Olen, Lille, Oud-Turnhout and Kasterlee center as explicit real-reference training AOIs.
• Kept Turnhout, Retie, Westerlo and Arendonk-heide as the documented validation holdouts.
• Added generated recommended_split provenance and tile-export validation that rejects unknown samples and manifest-backed holdout leakage.
• Hardened false-negative portfolio comparison to require identical reference feature identities, not only matching AOI names.

Local validation

• RED tests proved the expansion/split constants and validation guard were absent before implementation.
• python -m pytest backend/tests/test_sprint131_operator_sample_expansion.py backend/tests/test_sprint130_operator_yolo_tile_dataset.py backend/tests/test_sprint156_background_corpus_classification.py backend/tests/test_sprint170_detection_false_negative_audit.py -q: 23 passed.
• Official GRB OGC API probes returned building features at all four new AOI centers.
• Local sample generation was attempted but correctly stopped because the workstation Python lacks the existing GIS runtime extras; the all-in-one Tower runtime is the supported execution environment.

Tower runtime evidence

• Pushed 0f49c98 and redeployed the all-in-one runtime at http://192.168.10.150:1202; live migration and browser proxy verification passed.
• Refreshed /app/storage/operator-data/operator-samples-1024/operator_samples_manifest.json to schema version 2 with 20 sources.
• Newly fetched real GRB reference counts: Olen 1,952, Lille 1,839, Oud-Turnhout 2,691 and Kasterlee 1,831; all are recommended_split=train.
• Exported /app/storage/operator-data/yolo-building-aoi1024-expanded-minpx4vis035:
  • 171 retained tiles;
  • 153 positive and 18 negative tiles;
  • 45,892 labels;
  • 144 train and 27 validation tiles;
  • 9 low-variance negatives skipped;
  • validation holdouts Turnhout, Retie, Westerlo and Arendonk-heide recorded in summary provenance.
• Dataset audit status ok: no warnings, invalid labels or missing files; median normalized box area 0.000694274766, small-box share 0.3832694151486098.
• Improved the visual contact-sheet selector after the first live sheet overrepresented dense AOIs. The balanced rerun selected 40 tiles across all 19 retained source samples with zero invalid labels, missing images, missing label files or low-variance selections.
• Started one 50-epoch CPU YOLOv8s candidate as inactive runtime evidence: geointel-building-yolov8s-aoi1024expandedminpx4vis035e50.pt.

Known limitations

• No new model has been trained or activated.
• The inactive training run must finish and pass positive-AOI plus split-background promotion gates before it can be considered for activation.

Next recommended pass

• Finish the inactive candidate, run fixed-threshold positive evidence and split pure-empty/sparse-context background matrices, and preserve the current production default unless the promotion report passes every gate.

Sprint 172 - CPU AI image build hardening

What changed

• Reordered deploy/unraid/Dockerfile.all-in-one so pyproject.toml and minimal package metadata are installed before the complete backend source is copied.
• Code-only backend changes can now reuse the expensive GIS/AI dependency layer; dependency metadata changes still invalidate it.
• The opt-in CPU AI build now installs the same validated PyTorch 2.13.0 / torchvision 0.28.0 versions from the official CPU wheel index before installing the ai extra.
• Kept the full GIS and YOLO import/preflight smoke after the complete backend source copy.

Validation so far

• RED: the new Docker ordering/CPU-wheel regression test failed against the old Dockerfile.
• GREEN: python -m pytest backend/tests/test_docker_runtime_config.py -q: 26 passed.
• python -m pip index versions confirmed torch 2.13.0+cpu and torchvision 0.28.0+cpu are available from the configured CPU index for the workstation platform.

Remaining validation

• Build the AI-enabled all-in-one image on Tower after the current inactive model training run finishes, verify Torch reports a CPU build and rerun live migration/browser smokes before replacing the runtime.

Sprint 171.1 - Validation coverage provenance

• The expanded live export exposed that Arendonk-heide remained configured as a holdout while all of its low-variance tiles were correctly filtered out.
• Added retained_validation_sample_slugs and empty_validation_sample_slugs to tile dataset summaries so configured and actual validation coverage cannot be confused.
• Added a focused regression test and kept filtering behavior unchanged; no blank tile was reintroduced.

Sprint 173 - Expanded building model promotion

Runtime evidence

• Completed inactive 50-epoch CPU training for geointel-building-yolov8s-aoi1024expandedminpx4vis035e50.pt from the expanded 20-source real-data corpus.
• Trained-model SHA256: a8a79cf5b0bdc19a0245acc322cf77232c335e222bd5f3c00a17d5f29402c196.
• Training summary recorded 45,892 labels across 171 retained tiles; final training metrics were precision 0.55558, recall 0.35206, mAP50 0.27440 and mAP50-95 0.10107.
• The persisted seven-AOI positive matrix recommended tile size 512, overlap 64 and threshold 0.15: mean precision 0.6470590036, recall 0.4699913837, F1 0.5432865391, and minimum per-AOI F1 0.4897494305.
• The strict pure-empty gate covered Postel, Lommel and Arendonk across all tested thresholds and produced zero detections. Sparse-building contextual AOIs remained review-only evidence because real GRB buildings are present there.
• Fixed-threshold persisted false-negative comparison used identical reference feature populations and reduced the false-negative rate in all seven positive AOIs versus the previous active 0.35 profile.
• The guarded activation helper first returned ready_to_apply, resolved the exact local model asset and reported no downloads or inference. The reviewed --apply pass updated only GEOINTEL_INSTALL_AI, YOLO_ENABLED, YOLO_MODELS_DIR and YOLO_MODEL_PATH in the Tower environment.

UI and operator behavior

• Detection Lab now recommends the promoted expanded-AOI model at threshold 0.15 and surfaces its seven-AOI coverage plus QA metrics.
• The previous AOI1024 background-aware model remains mounted as an explicit legacy high-precision 0.35 review profile.
• Profile selection remains deliberate: it selects a mounted local asset and run threshold only; it does not mutate runtime environment, download a model or start inference automatically.

Remaining limitation

• Persistent small-building misses remain the weakest quality bucket. Continue with targeted evidence review and data coverage before considering another training run; do not infer production accuracy from aggregate F1 alone.

Tower deployment verification

• Pushed commit 1c16313 and rebuilt the all-in-one runtime at http://192.168.10.150:1202.
• The opt-in AI image installed torch 2.13.0+cpu and torchvision 0.28.0+cpu from the official CPU wheel index; runtime CUDA availability is false. Ultralytics reports version 8.4.93.
• The live model catalog exposes 23 local files and marks only geointel-building-yolov8s-aoi1024expandedminpx4vis035e50-pt active with the expected SHA256.
• Explicit --check-model-load preflight passed against an existing nine-tile manifest: dependencies, model load, manifest structure, tile paths and tile limit were all valid; no inference or download ran.
• Embedded PostGIS live migration smoke passed with PostGIS 3.6, required tables/indexes, database collation and the single Alembic head 202606120900.
• Browser validation confirmed that the recommended profile selects the exact active asset and threshold 0.15, while live preflight displays CPU dependency/model readiness and the expected missing-manifest guard before dataset handoff.
• Browser console warnings/errors: 0.

Sprint 174 - Focused small-building recovery and promotion

Data and training evidence

• Converted the Sprint 173 persistent false-negative audit into one focused real-data experiment instead of extending the same corpus blindly.
• Added Beerse, Rijkevorsel, Hoogstraten and Vorselaar as training AOIs and Vosselaar/Grobbendonk as independent tile-level validation AOIs.
• Kept Turnhout, Retie and Westerlo outside the tile corpus as operation-level holdouts.
• Exported /app/storage/operator-data/yolo-building-aoi1024-smallbld-minpx3vis035 from an explicit 23-sample manifest subset:
  • 198 retained tiles;
  • 180 positive and 18 negative tiles;
  • 58,820 real GRB-derived labels;
  • 48 visually reviewed tiles;
  • zero invalid labels, missing images, missing label files or low-variance review selections.
• The accepted min-label-px=3 corpus retained 1,228 more genuine small-building labels than the comparable min-label-px=4 export.
• Trained one inactive 30-epoch CPU candidate from the previous active local model:
  • model: geointel-building-yolov8s-smallbld-minpx3-img640-ft30.pt;
  • model SHA256: a9088b8491dfae36694b53e9e9406cb4e3511d334a5712fa34f75078a47759c1;
  • dataset-summary SHA256: 49b2a07d2105d08356431757b83eafc1498eaf1fb76965b1efe05b776824942a;
  • dataset-YAML SHA256: 3a2ea97c35a18072a1ab6738cd673c0ecec5344b19461c91d72a15e138d46e8d;
  • no model download and no fake training or QA data.

Persisted promotion evidence

• Evaluated the exact fixed profile tile=512, overlap=64, confidence=0.15 with QA match IoU explicitly fixed at 0.25.
• Seven positive AOIs produced:
  • mean precision 0.5898197518;
  • mean recall 0.5769921004;
  • mean F1 0.5824578632;
  • minimum per-AOI F1 0.5527837436.
• Every AOI improved F1 relative to the previous balanced model. Turnhout improved from 0.4897494305 to 0.5527837436.
• The strict pure-empty gate covered Postel, Lommel and Arendonk and produced zero detections for every sample.
• The formal promotion report recommended the exact key geointel-building-yolov8s-smallbld-minpx3-img640-ft30-pt|512|64|0.15.
• Fixed-reference object evidence used identical GRB feature identities and reduced false negatives from 7,753 to 6,182:
  • 1,571 fewer total false negatives;
  • 745 fewer misses in the 25-100 m2 bucket;
  • 181 fewer misses below 25 m2;
  • all seven AOIs improved.
• Remaining persistent misses total 5,838, concentrated in Turnhout, Herentals and Geel and still dominated by small buildings.
• Mean precision decreased from 0.6470590036 to 0.5898197518. The new profile is therefore a recall-balanced operator default with a higher false-positive review load, not ground truth.

Repository hardening

• Added explicit --samples / OPERATOR_YOLO_SAMPLES corpus selection with selected/excluded sample provenance and unknown-sample rejection.
• Added persistent false-negative area statistics, size buckets and combined GeoJSON review evidence.
• Copied the complete operator evaluation/promotion toolchain into the all-in-one image and added a regression that rejects every Docker COPY scripts/... source that does not exist.
• Removed Pydantic protected-namespace warnings for legitimate model_* API fields while preserving all schema field names and response contracts.
• Updated Detection Lab profiles: the new small-building profile is recommended, the previous expanded profile remains the higher-precision legacy choice, and the background-aware 0.35 profile remains conservative.
• Live 2560x1080 inspection found that two-panel AI/QA/Export workspaces inherited four ultrawide columns and left half of the main canvas empty. Those workspaces now remain explicit two-column grids while the three-panel Data workspace keeps three columns.
• The all-in-one dependency layer now copies only pyproject.toml plus a stable build-only package README before installation; the real backend/README.md still enters with the complete backend source, so documentation-only edits no longer invalidate Torch/GIS dependencies.
• Guarded activation first returned ready_to_apply; the reviewed --apply pass updated only GEOINTEL_INSTALL_AI, YOLO_ENABLED, YOLO_MODELS_DIR and YOLO_MODEL_PATH in the Tower environment.

Local validation

• python -m compileall backend/app: passed.
• python -m pytest: 472 passed.
• python -m ruff check for all changed Python modules/tests: passed.
• npm run typecheck: passed.
• npm run build: passed; app bundle 215.64 kB, MapLibre bundle 801.82 kB before gzip.
• bash scripts/run_readiness_check.sh: passed with 472 tests.
• python -m alembic heads: one head, 202606120900.
• python -m alembic upgrade head --sql: complete migration chain rendered successfully.
• Shell syntax checks passed for live migration and the full operator evaluation/promotion chain.

Next recommended pass

• After redeploy, verify the active model SHA, local model-load preflight, live PostGIS migration smoke and browser profile selection. Then review false-positive evidence and the remaining 5,838 persistent misses before any further training.

Sprint 175 - Detection result scale and false-positive evidence review

UI hardening

• Confirmed that Detection Lab rendered every persisted detection row at once; Westerlo alone produced 1,172 body rows in the browser.
• Added local 25/50/100-row pagination with a default of 50 rows, bounded page controls and automatic page-one reset after run/filter/result changes.
• Live visual inspection exposed tall rows caused by full container paths; source-tile cells now show the filename and retain the full persisted path as a tooltip.
• Kept the full persisted collection unchanged for the existing MapLibre GeoJSON overlay and detection QA/QC. No endpoint, response envelope or persistence contract changed.

Persisted false-positive evidence

• Added scripts/audit_detection_false_positive_evidence.py as a read-only evidence consumer.
• The audit validates FeatureCollection/polygon geometry, compares declared portfolio role counts with actual evidence, computes WGS84 geodesic areas, preserves original feature provenance and emits combined false_positives.geojson.
• Source tile summaries are qualified by AOI because tile_index is local to each raster manifest.
• The active fixed-threshold seven-AOI portfolio produced:
  • 5,568 false positives among 13,613 candidate detections (0.4090 false-positive share);
  • median false-positive geometry area 184.5 m2, p90 607.7 m2;
  • 52 below 25 m2, 1,382 between 25-100 m2, 3,412 between 100-500 m2 and 722 at or above 500 m2;
  • largest AOI review volumes: Turnhout 1,102, Herentals 917, Geel 913;
  • largest AOI-qualified tile hotspot: turnhout:0 with 236 false positives.
• Existing persisted QA evidence carries run threshold and tile index but no per-detection confidence. The audit reports confidence coverage 0/5,568 and does not invent confidence statistics.
• Added focused regression coverage, readiness compilation and all-in-one image inclusion. No training, inference, provider fetch, model download or activation occurred.

Local validation

• python -m compileall backend/app: passed.
• python -m pytest: 475 passed.
• python -m ruff check for the new audit/test modules: passed.
• npm run typecheck: passed.
• npm run build: passed; app bundle 217.00 kB, MapLibre bundle 801.82 kB before gzip.
• bash scripts/run_readiness_check.sh: passed with 475 tests and all release-critical syntax gates.
• python -m alembic heads: one head, 202606120900.
• python -m alembic upgrade head --sql: complete migration chain rendered successfully.
• Local docker compose config could not run because Docker CLI is not installed on the Windows host; live image/PostGIS validation is delegated to the Docker-enabled Tower deployment.

Tower deployment verification

• Pushed runtime commits 4455e24 and d188014; Tower rebuilt the all-in-one image from main with the GIS/CPU-AI dependency layer cached.
• Live PostGIS migration smoke passed with PostGIS 3.6, required runtime tables/indexes and single Alembic head 202606120900.
• The active local model remained /app/models/geointel-building-yolov8s-smallbld-minpx3-img640-ft30.pt with SHA256 a9088b8491dfae36694b53e9e9406cb4e3511d334a5712fa34f75078a47759c1.
• Local model-load preflight passed against a persisted nine-tile manifest with Torch 2.13.0+cpu, Ultralytics 8.4.93, CUDA disabled, no inference and no download.
• The new false-positive audit ran successfully inside the live all-in-one container against the persisted seven-AOI evidence portfolio.
• Browser verification against the persisted Westerlo run confirmed 1,172 loaded detections, 50 rendered rows, 1-50 of 1172, page 1/24, working next-page and 100-row controls, and reset back to the 50-row default.
• Final cells display tile_0001.tif while the full persisted path remains available through the title attribute.
• At 2560x1080, the Detection results surface used about 871 px, its table client/scroll widths both measured 827 px and the document did not overflow the viewport.
• Browser console warnings/errors: 0.

Next recommended pass

• Visually classify a stratified false-positive sample from Turnhout, Herentals and Geel before deciding whether any confirmed examples belong in a new hard-negative corpus. Review the remaining 5,838 persistent false negatives in the same evidence-led pass; do not start another blind training run.

Sprint 176 - Detection false-positive visual review gate

Persisted provenance

• Extended the existing read-only QA evidence GeoJSON conversion so detection-backed evidence carries the persisted detection id, job id, confidence, model name/version, source tile path and pixel bbox.
• Added equivalent persisted segmentation provenance fields without changing the endpoint, canonical envelope, ORM or migration chain.
• Historical QualityCheck evidence can be re-exported against existing persisted Detection rows; no QA rerun or data rewrite is required.

Manual visual review

• Added a storage-root-confined contact-sheet renderer for persisted detection false-positive evidence.
• The renderer validates portfolio role counts, polygon geometry, source imagery and persisted provenance, then selects deterministically across AOI, WGS84 area bucket and confidence band.
• Source imagery is rendered with the candidate pixel bbox plus persisted matched-reference and missed-reference overlays.
• Live orthophoto inspection exposed stretched non-square edge tiles and overly distant full-tile context; the renderer now uses candidate-centred crops, preserves aspect ratio and limits reference overlays to the crop.
• Added an explicit five-state review CSV: confirmed_model_false_positive, reference_gap_or_change, qa_alignment_mismatch, uncertain and unreviewed.
• Added a separate validator that rejects missing, duplicate, unexpected or invalid decisions. --require-complete exits with code 2 while any record remains unreviewed.
• Only explicitly confirmed model false-positives are emitted to confirmed_model_false_positives.geojson; no QA result is automatically converted into a model label or training artifact.

Validation

• python -m compileall backend/app: passed.
• python -m pytest: 478 passed.
• Focused provenance/render/path-confinement/incomplete-review/export tests: passed.
• python -m ruff check for changed Python services, scripts and tests: passed.
• Generated fixture contact sheet was visually inspected at 128 px thumbnails; candidate/reference/missed-reference overlays and header provenance remained readable.
• npm run typecheck: passed.
• npm run build: passed; app bundle 217.00 kB, MapLibre bundle 801.82 kB before gzip.
• bash scripts/run_readiness_check.sh: passed with 478 tests and the new operator-script compile gates.
• python -m alembic heads: one head, 202606120900.
• python -m alembic upgrade head --sql: complete migration chain rendered successfully.
• Local Docker validation remains unavailable because Docker CLI is not installed on the Windows host; live all-in-one/PostGIS validation follows on Tower after deployment.

Tower deployment evidence

• Deployed commit 1322a5d through the repository-driven all-in-one build with the existing CPU-AI dependency layer cached.
• PostGIS 3.6, required tables/indexes, Alembic head 202606120900, frontend proxy, API proxy and icon checks passed.
• Re-exported all seven persisted fixed-threshold QualityChecks into an enriched portfolio containing 27,840 evidence features.
• The false-positive audit now reports persisted confidence coverage 5,568/5,568 rather than inventing values for the older static export.
• Rendered an initial 48-case Turnhout/Herentals/Geel review spanning all four area buckets and all three confidence bands with no missing provenance or source tiles.
• All 48 decisions remain explicitly unreviewed; validation status is review_required and the confirmed-model-false-positive GeoJSON is empty.

Next recommended pass

• Re-export the seven-AOI evidence portfolio from the deployed backend, render the Mol/Geel/Turnhout sheets and inspect the real orthophoto evidence. Keep all CSV decisions unreviewed until an operator makes an explicit visual classification; do not start another model training run yet.

Sprint 177 - Mol-first operating context

Product and workbench focus

• Declared Mol as the primary operational context while preserving the broader Kempen for cross-area validation and regional interoperability.
• Centralized Mol coordinates, default region and a compact 1 km EPSG:4326 MultiPolygon AOI in the frontend focus configuration.
• Initial project discovery now recognizes Mol from project metadata or persisted dataset provenance and inspects those candidates first.
• Explicitly selected and newly created projects still take precedence, so the focus rule does not fight operator intent.
• The empty MapLibre workbench starts over Mol; loaded AOIs and data continue to determine map bounds normally.

Operator provenance

• Moved Mol to the first position in default real-data sample preparation without dropping any existing Kempen sample.
• Multi-sample quality runs now forward their AOI slug, and future persisted matrix project names retain that origin instead of appearing as anonymous model/tile runs.
• No provider fetch, AI output, QA metric, migration, API contract or model configuration was changed.

Validation

• Focused Mol-primary regression coverage: 3 passed.
• bash scripts/run_readiness_check.sh: passed with 481 backend tests, API-contract audit, single Alembic head, frontend typecheck and production build.
• Frontend production bundles: app 217.69 kB, React vendor 140.74 kB, MapLibre 801.82 kB before gzip.
• python -m alembic upgrade head --sql: complete migration chain rendered through single head 202606120900.
• Both changed matrix scripts pass bash -n; no migration file changed.

Tower deployment evidence

• Deployed commit e8eecb2 through the repository-driven all-in-one build on port 1202 with the existing CPU-AI dependency layer cached.
• Live migration smoke passed with PostGIS 3.6, required runtime tables/indexes and single Alembic head 202606120900.
• Renamed persisted project d25206c0-dcba-46e5-aba9-66ace1122a30 to an explicit Mol operating context and added a real 1 km Mol AOI (999,796.69 m2) without changing its datasets or analysis results.
• Live Mol context contains a ready EPSG:31370 1024x1024 orthophoto and 1,993 ready EPSG:4326 GRB reference buildings.
• Persisted detection QA remains unchanged: precision 0.6011, recall 0.5891, F1 0.5950, mean IoU 0.4554, 779 false positives and 819 false negatives.
• In-app browser verification selected the Mol project, Mol AOI and Mol reference dataset automatically. MapLibre rendered the existing 1,953-feature detection layer over the road basemap with one canvas, zero browser warnings/errors and no horizontal document overflow at 1265x720.

Sprint 178 - Mol multi-zone operational validation

Implementation

• Added Mol center, Achterbos, Gompel, Donk and Postel as explicit operator contexts with municipality and operational-zone provenance.
• Marked Achterbos, Gompel, Donk and Postel as validation holdouts to keep them outside future training exports unless the split policy is deliberately changed.
• Kept Postel-bos as a separate background control, so an empty/sparse context is never assigned fabricated precision, recall or F1.
• Extended the existing real-data workflow with optional project region and EPSG:4326 AOI bounds; manifest-backed positive and background projects now open map-ready with persisted Areas.
• Added run_mol_operational_validation.sh to compose the existing positive QA matrix and background detection-pressure matrix and emit one evidence summary.
• Included the new runner in the all-in-one runtime and release-readiness syntax gate. No API route, ORM model, migration, model activation or frontend behavior changed.

Initial validation

• Focused Mol/operator/workflow/Docker regressions: 43 passed.
• Changed Python operator preparer compiled successfully.
• All five affected shell workflows passed bash -n.
• git diff --check: clean apart from the existing Windows line-ending notice for the all-in-one Dockerfile.
• bash scripts/run_readiness_check.sh: passed with 485 backend tests, contract audit, single Alembic head, frontend typecheck and production build.
• Frontend bundle sizes are unchanged: app 217.69 kB, React vendor 140.74 kB and MapLibre 801.82 kB before gzip.

Live Tower completion

• Deployed through Gitea commit 720ad71; the all-in-one browser/API smoke, PostGIS 3.6, required runtime schema and Alembic head 202606120900 passed.
• Prepared real 1024 px orthophoto/GRB samples for Mol center, Achterbos, Gompel, Donk, Postel and Postel-bos. Positive reference counts are 1,993, 1,388, 1,070, 1,234 and 137; the background control contains exactly 0 GRB buildings.
• Fixed two manifest-path type errors found by the first live multi-sample aggregation and moved all-in-one evidence defaults from the replaceable container layer to /app/storage/operator-evidence.
• Completed the persistent four-zone configured-YOLO benchmark at confidence 0.15, tile 512, overlap 64 and QA IoU 0.25: mean precision 0.6286, mean recall 0.5370, mean F1 0.5768, 2,239 matches, 1,215 false positives and 1,590 false negatives.
• Per-zone F1 is Achterbos 0.6433, Gompel 0.6333, Donk 0.5823 and Postel 0.4483. Postel is the explicit weakest-zone review priority; no automatic promotion or retraining decision was made.
• The real empty Postel-bos control produced 0 detections and therefore 0.0 false-positive pressure without fabricated QA metrics.
• Verified through the browser that the complete Postel project wins initial selection over the newer raster-only background project, with one AOI, one ready raster, one ready GRB reference, one detection run, one QualityCheck and one export.
• MapLibre rendered the OSM road basemap, AOI and 95 persisted detections without browser warnings or horizontal overflow at 1280x720 and 2560x1080; the ultrawide canvas measured 1772x830 CSS pixels.
• Ran the interactive PostGIS AOI query against vector_features: 126 GRB buildings returned with no truncation. The guided GIS smoke persisted a derived dataset and GeoJSON export, then produced candidate/reference QA F1 0.9582 with 126 matches, 0 false positives and 11 false negatives; 263 persisted evidence features rendered back on the map.
• Remaining evidence caveat: the dataset QA result reports weak CRS-assumption warnings, so those geometry metrics remain explicitly approximate until CRS provenance handling is reviewed.
• Host observation outside GeoIntel: Unraid recovered after reboot and serves the app, but still reports one disabled/invalid array device. Storage administration should resolve that independently of application development.

Sprint 179 - Mol Donk/Postel detection evidence diagnosis

Implementation

• Added render_detection_false_negative_review_contact_sheets.py as a read-only counterpart to the persisted false-positive review workflow.
• Resolved each sample's exact persisted tile manifest from its fixed-threshold run summary; manifests and source tiles are confined to /app/storage.
• Projected WGS84 missed-reference geometry onto the real inference tiles and rendered nearby persisted candidate detections plus matched-reference context.
• Added deterministic AOI/area stratification and an explicit five-state manual decision CSV. No decision is inferred and no training input is exported automatically.
• Separated references outside every persisted source tile into false_negatives_outside_tile_coverage.geojson instead of hiding them or calling them model misses.
• Added focused rendering, manifest, source-coverage and storage-confinement regression tests; wired the script into readiness compilation and the all-in-one image.

Live Mol evidence

• Re-exported the four-zone fixed-threshold portfolio from existing persisted QualityChecks without rerunning inference or mutating application data.
• The complete portfolio contains 7,283 evidence features. Donk contributes 424 false positives and 553 false negatives; Postel contributes 43 and 85.
• Rendered and inspected 48 stratified false-positive cases over Donk/Postel. Explicit decisions: 37 QA alignment mismatches, 7 confirmed model false positives, 3 reference gaps/changes and 1 uncertain. The existing validator passed with status complete.
• Rendered and inspected 48 stratified false-negative cases. Explicit decisions: 27 QA alignment mismatches, 6 confirmed model false negatives, 3 reference gaps/changes and 12 imagery-obscured/uncertain.
• Found 41/638 false-negative evidence records outside every persisted inference tile: Donk 28, Postel 13. Directionally excluding those records raises Donk recall from 0.5519 to 0.5647 and Postel from 0.3796 to 0.4194; these are audit diagnostics only and no persisted metric was changed.
• The dominant visual mode is rectangle-to-footprint mismatch on large industrial roofs and dense residential blocks, often with a blue persisted candidate already overlapping the red missed GRB footprint. Postel additionally contains many tiny/vegetation-obscured references.
• Persistent evidence and the assessment are stored below /app/storage/operator-evidence/mol-operational-review/20260713.

Decision and next pass

• NO-GO for immediate retraining. Only 7/48 reviewed false positives and 6/48 reviewed false negatives were confirmed model errors; evaluation alignment and coverage defects dominate the selected evidence.
• Next harden detection QA to restrict candidate/reference populations to persisted raster/tile coverage and expose best-IoU/overlap/unmatched diagnostics. Rerun Donk/Postel QA against the unchanged persisted detections before deciding whether the confirmed model-error subset justifies curated training.

Validation

• Focused false-positive/false-negative audit and visual-review coverage: 8 passed.
• Ruff passed for the changed Python renderer and regression tests.
• bash scripts/run_readiness_check.sh: passed with 487 backend tests, the 81-route API contract audit, one Alembic head, frontend typecheck and production build.
• python -m alembic upgrade head --sql rendered the complete migration chain through 202606120900; no migration changed.
• Local docker compose config was unavailable because the Windows workstation has no Docker CLI. The repository-driven Tower deployment remains the required live Docker validation.

Tower deployment evidence

• Pushed and deployed commits 50952e1 and 191d7aa through the repository-driven all-in-one flow with the existing CPU AI dependency layer cached.
• Tower docker compose -f docker-compose.unraid.yml config, live PostGIS migration smoke, required schema/index checks and Alembic head 202606120900 passed.
• The definitive image contains the false-negative renderer under /app/scripts; persisted review artifacts remained available after both container replacements.
• Precomputing source-tile footprints in EPSG:4326 reduced the deployed 12-card Donk/Postel smoke from minutes to 2.06 s while preserving 638 evidence records, 597 reviewable records and 41 explicit outside-coverage exclusions.
• Browser verification on http://192.168.10.150:1202 selected the complete Mol Postel workspace and showed ready project/AOI/dataset/QA/export state.
• The Map workspace rendered one 1033x542 MapLibre canvas, the OSM road basemap, AOI and 95 persisted detections with no browser warnings/errors, no visible dialog and no horizontal document overflow at a 1280 px viewport.
• No API contract, migration, QualityCheck/Metric row, Detection row, model asset, active-model configuration or inference result changed in this pass.

Sprint 180 - Premium workbench UX hardening

Implementation

• Reworked the workbench shell into a calmer operational hierarchy with grouped Workspace, Analyze and Deliver navigation, a compact Mol/Kempen context header and a centered content canvas.
• Replaced the permanently reserved inspector column with an on-demand detail drawer. Dataset inspection still invokes the existing detail-loading callback and now opens the drawer explicitly.
• Made Data operational at scale by disclosing create/upload forms on demand, arranging Project, AOI and Dataset panels side by side on desktop and bounding their long collections with internal scrolling.
• Promoted MapLibre to the primary Map surface. Layer provenance and low-frequency BBox/raw-feature controls remain available in collapsed detail surfaces without removing any GIS action.
• Reordered Detection and Segmentation Labs around run controls and result review; model registry and YOLO preflight remain fully available as secondary disclosures.
• Added a dedicated premium.css presentation layer with a restrained neutral/teal palette, consistent controls, stable panel dimensions and an actual full-width mobile shell. The former narrow sidebar-plus-content split is removed below 920 px.
• Added focused static regression coverage for navigation grouping, optional inspector wiring, scalable Data panels, map hierarchy, AI Lab ordering and responsive behavior.

Behavior preservation

• Existing React state hooks, API clients, service calls, map callbacks, QA actions, uploads, exports and AI run handlers were retained.
• No backend application code, endpoint contract, database model, migration, geospatial algorithm, model configuration or persisted evidence was changed.
• Legacy UI contract strings and callback signatures remain present for the existing Sprint 1-179 regression suite.

Visual audit

• At 1280x720, the Overview now uses one compact readiness row and no permanent inspector reservation; Map controls, canvas and secondary layer details follow the intended visual order.
• At 2560x1080, the main workbench uses a centered 1680 px maximum content width while the map can expand independently; the previous permanently empty 416 px inspector column is gone.
• At 390x844, navigation becomes a horizontal full-width rail and the main workspace occupies the viewport instead of sharing it with a 152 px sidebar.
• A live 50-project Data state now keeps project and dataset panels within the viewport with internal scrolling instead of pushing the Dataset panel thousands of pixels below the fold.
• Detection and Segmentation run controls appear before registry diagnostics, reducing the distance to the primary task while retaining honest model-state detail.

Local validation

• React best-practice review found no new conditional hooks, effect synchronization, unstable list keys or non-semantic interactive controls.
• Focused premium-workbench and legacy UI regression coverage passed.
• python -m compileall backend/app: passed.
• Full backend suite: 492 passed.
• Frontend typecheck and production build: passed; app bundle 219.76 kB, React vendor 140.74 kB and MapLibre 801.82 kB before gzip.
• bash scripts/run_readiness_check.sh: passed with the 81-route API contract audit, single Alembic head, all backend tests, frontend typecheck/build and live-smoke syntax gate.

Tower deployment evidence

• Pushed and deployed commit a2d9cef through the repository-driven all-in-one flow on port 1202.
• The production image rebuilt the frontend successfully and retained the existing cached CPU AI/GIS dependency layer.
• Live PostGIS 3.6, database collation, required tables/indexes and the single Alembic head 202606120900 passed the migration smoke.
• The frontend, proxied projects API and Unraid icon passed the deployed browser runtime smoke.
• At 1280x720, the populated Data workspace rendered three bounded columns; the 50-project panel remained 552 px high with internal scrolling instead of extending its 8575 px content into the document.
• At 2560x1080, MapLibre rendered one nonblank 1678x734 canvas in a centered 1680 px map surface with no permanently reserved inspector.
• At 390x844, the sidebar became a 375 px full-width horizontal navigation rail, the main workspace remained 375 px wide and document width stayed within the viewport. The detail drawer opened full-screen and closed correctly.
• The live Map workspace preserved layer selection, AOI controls and persisted 95-feature detection overlay. Browser console verification returned zero warnings and zero errors across desktop, ultrawide, mobile, Data, Map and AI Labs checks.

Next pass

• Harden detection QA coverage and matching diagnostics before making a retraining decision; keep that work separate from this presentation-only sprint.

Sprint 181 - Complete Mol municipality workspace

Implementation

• Added an explicit operator provisioner for the official VRBG Refgem municipality geometry for Mol (NIS 13025) and the complete paged GRB GBG building collection clipped to that exact boundary.
• Added deterministic persistent source artefacts and a manifest containing source URLs, checksums, page/feature counts, boundary bounds and area, and an explicit truncation flag. Pagination and identity checks fail closed.
• Declared EPSG:4326 in both generated GeoJSON FeatureCollections so the importer records crs_assumed=false for this known official OGC source.
• Added bounded retries for safe source GETs after the live refresh exposed a transient GRB HTTP 500 on page 66. Mutating GeoIntel API requests are not retried automatically.
• Kept the provider boundary honest: provisioning is an operator action and imports through canonical Project, Area and Dataset HTTP routes. It does not enable the dormant live GRB provider or write directly to vector_features.
• Optimized the existing vector persistence path by replacing one ORM refresh per feature with a single flush and commit. Persistence shape and API behavior remain unchanged.
• Made Mol Municipality Workbench the preferred fresh-session context once its official boundary is ready. The boundary opens first; the much larger building layer remains explicitly selectable from the Map database-layer control.
• Replaced spread-based map extent calculations with a streaming, memoized GeoJSON bounds helper and added municipality/building layer styling. This avoids large coordinate arrays while keeping the existing MapLibre path.

Initial validation

• Exact clipping, pagination/truncation, persistence-scaling and frontend wiring regression coverage passed locally.
• Final full backend suite and readiness gate passed with 497 tests and the 81-route API contract audit. Frontend typecheck/build, Ruff, shell syntax, single Alembic head 202606120900 and the complete offline migration chain also passed.
• No API route, ORM model, migration, QA metric, detection result or model configuration changed.

Tower and live GIS evidence

• Pushed and deployed the implementation through commits 490325e, 480634c and e8a35b9; live PostGIS 3.6, required schema/indexes, Alembic head, frontend proxy, API proxy and icon checks passed.
• Provisioned definitive project d74c1f87-29c0-4c67-adfc-560764f2b80e and official area 6551ee61-0bc7-47cd-8e38-94aefb815997 with bounds [5.035357, 51.1534809, 5.24218961, 51.32265943] and projected area 114.546677 km2.
• The complete source bbox returned 89,153 unique GRB candidates over 90 pages. Exact municipality clipping retained 36,941 buildings, rejected 52,212 outside features and clipped 24 boundary-crossing features. The manifest is complete and explicitly reports reference_truncated=false.
• PostGIS contains 36,941 vector feature rows and 36,941 distinct source IDs, with zero invalid geometries and zero non-EPSG:4326 geometries. Strict coverage reports 18 floating-point boundary artefacts whose combined outside area is only 5.75e-9 m2; no material building area falls outside Mol.
• The definitive boundary dataset a8be7de2-e9e1-437a-959c-a18d9a454886 and building dataset 9b829bcb-eff5-48c2-baf9-ff6f8675fe7e both persist EPSG:4326 with crs_assumed=false. The retry-hardened refresh completed all 90 pages after the earlier transient source failure.
• A fresh browser session selected the municipality project, official area and one-feature boundary automatically instead of a historical Postel validation context. Selecting the complete building layer rendered all 36,941 features and a live AOI PostGIS query returned 250 persisted features with dataset/export actions enabled.
• Browser verification at 1280x720 and 2560x1080 found one nonblank MapLibre canvas, no console warnings/errors and no horizontal overflow. The widescreen map canvas measured 1678x734 CSS pixels.

Next pass

• Add viewport/zoom-aware vector delivery before expanding beyond Mol or layering additional municipality-wide reference classes. Then acquire and tile georeferenced imagery only for an explicitly selected Mol analysis zone and run configured-YOLO plus QA/QC against the persisted GRB reference.

Sprint 182 - Municipality viewport delivery and bounded AI handoff

Implementation

• Added useViewportVectorLayer with centralized feature threshold, minimum zoom, response limit and debounce policy. Vector datasets above 5,000 features now use the existing canonical PostGIS bbox-selection endpoint from zoom level 14 instead of downloading the full stored GeoJSON file.
• Added MapLibre moveend viewport reporting, stale-request protection and disabled automatic fit for viewport slices. AOI changes still frame the map; small vectors and persisted analysis/evidence layers retain their existing complete-layer fit behavior.
• Added explicit low-zoom, loading, visible/total, truncation and error states. A selected large database layer is no longer presented as an empty or missing layer before the first detail request.
• Extended the real-data detection/QA workflow with validated existing-project reuse, safe distinct upload filenames and actual Area linkage for the raster and matching reference vector. The workflow still consumes operator-provided files and an existing local model asset; it performs no provider fetch or model download.

Local validation

• python -m compileall backend/app passed.
• Full backend suite passed: 500 tests.
• npm run typecheck and npm run build passed; the production build contains 85 transformed modules and preserves the dedicated MapLibre chunk.
• bash scripts/run_readiness_check.sh passed with the 81-route API contract audit, one Alembic head 202606120900, shell syntax checks and full frontend build.
• Internal browser validation against the local frontend and live Tower API selected mol_grb_gbg_buildings.geojson, showed the zoom-14 PostGIS delivery guard, suppressed the incorrect empty-layer state and loaded an actual viewport slice with 2 visible of 36,941 total features. No browser warnings or errors were recorded.

Sprint 183 - Mol map source clarity and live AI validation

Runtime evidence

• Deployed commit 1ba479b to the all-in-one Tower runtime at http://192.168.10.150:1202. The deployment rebuilt the updated postgres:16-bookworm GIS runtime and installed the configured CPU AI image.
• Live migration smoke passed against PostGIS 3.6 with required runtime schema objects, geometry indexes and the single Alembic head 202606120900. /health reported service, backend version and database as healthy.
• Reused project d74c1f87-29c0-4c67-adfc-560764f2b80e and created bounded area 0a38dd7a-d55a-4453-ae59-db8579ed8170 for Mol Centrum - AI analysezone 500m.
• Persisted raster dataset e9a292e2-57d2-4a85-9df6-32580ba6ea98 and GRB reference dataset bfc83882-2acc-470d-baec-ee828b4428f8 through the canonical upload and vector persistence paths.
• Ran local model asset geointel-building-yolov8s-smallbld-minpx3-img640-ft30-pt against the persisted tile manifest. Analysis run 97c204cc-bc75-4139-aab5-25397dd0d87e persisted 36 detections.
• Quality check 1edb81ec-6799-41c1-ae25-9145f52407bd persisted 17 matches, 19 false positives and 357 false negatives. Persisted metrics were precision 0.472222, recall 0.045455, F1 0.082927 and mean IoU 0.595803; the workflow works, but these figures do not justify production model acceptance.
• Detection export d18946c4-b7c5-4262-83ad-6c406c6aa59c was persisted.

Map-source correction

• Live browser review exposed that the automatically selected detection run could mask an explicitly selected database layer and leave the header at 36 features. Added a Database/Analysis result source mode and made database selection return to Database mode.
• Local browser verification then selected the complete mol_grb_gbg_buildings.geojson dataset, rendered a real 1,000-feature PostGIS viewport slice over the OpenStreetMap road basemap, displayed the truncation/zoom guidance, switched to the 36-feature detection overlay and back without state loss, and recorded no console errors.

Next pass

• Clip detection QA reference populations to actual raster/tile coverage and add box-to-building-footprint matching diagnostics before any further model training or promotion decision.

Sprint 184 - Detection QA coverage and matching diagnostics

Implementation

• Added a focused detection-QA coverage service that resolves the persisted tile manifest from the analysis run, validates dataset ownership and CRS, transforms each tile footprint to EPSG:4326 and unions the exact inference coverage.
• Configured-YOLO QA now fails closed when persisted manifest provenance is absent, invalid or belongs to another raster. Explicit fixture and legacy runs without a manifest retain their existing unbounded QA behavior.
• Candidate Detection geometries and persisted reference VectorFeature geometries are clipped to inference coverage before the existing one-to-one IoU matcher runs. Raw, evaluated, excluded-outside and boundary-clipped counts are persisted in quality_checks.findings_json.
• Canonical precision, recall, F1, mean IoU and metric rows remain strict candidate-polygon versus reference-footprint results. A second candidate-polygon versus reference-envelope pass is persisted and displayed as diagnostic-only evidence; it never replaces or promotes canonical metrics.
• Detection Lab now explains which reference population was evaluated and clearly separates possible box-to-footprint artifacts from the canonical scorecard.
• Updated the real-data operator smoke assertions, API/AI/database contracts and backend/frontend operator documentation. No migration, request contract, provider fetch or model dependency changed.

Local validation

• bash scripts/run_readiness_check.sh passed end to end.
• Full backend suite passed: 509 tests after the final coverage, manifest provenance, framework-contract and frontend-toolchain regression additions.
• API contract audit passed with 81 implemented routes and the two documented non-envelope endpoints.
• Alembic reports one head: 202606120900.
• Frontend TypeScript checking and Vite 7 production build passed with 82 modules; the dedicated MapLibre chunk remains intact.

Next pass

• Deploy Sprint 184 to Tower, rerun QA for the persisted Mol-center analysis run and verify both persisted coverage evidence and Detection Lab rendering against live PostGIS before making any model-training decision.

Tower and live Mol evidence

• Pushed commit 948e50b and rebuilt the all-in-one runtime at http://192.168.10.150:1202. PostGIS 3.6, required schema/indexes, database collation, Alembic head 202606120900, frontend proxy, API proxy and icon checks passed.
• Reused persisted analysis run 97c204cc-bc75-4139-aab5-25397dd0d87e, raster e9a292e2-57d2-4a85-9df6-32580ba6ea98 and GRB reference dataset bfc83882-2acc-470d-baec-ee828b4428f8; no duplicate inference or fake data was produced.
• The persisted EPSG:31370 tile manifest contained one inference tile. All 36 detections were evaluated; one was clipped at the tile boundary. Of 374 persisted reference footprints, 304 intersected coverage, 70 were excluded outside coverage and 34 were clipped at the boundary.
• Quality check a25290ed-14ea-4c84-aff7-20672f568b1c persisted 19 strict footprint-IoU matches, 17 false positives and 285 false negatives. Its six canonical Metric rows contain precision 0.527778, recall 0.0625, F1 0.111765, mean IoU 0.599302 and the two error counts.
• The diagnostic-only reference-envelope pass found 33 matches and identifies 14 possible box-to-footprint matching artifacts. Those values are persisted only in findings and do not alter canonical Metric rows.
• Internal-browser validation reran the comparison from Detection Lab and displayed 304 of 374 reference features evaluated, coverage exclusions, boundary clipping and the explicitly labelled diagnostic. The deployed page produced no console warnings or errors for the live Tower URL.
• A later clean framework resolution exposed FastAPI 0.139.0's grouped top-level router representation: app.routes contains 17 groups while the canonical OpenAPI schema still contains all 81 operations. Updated the API contract audit to read the OpenAPI path map and added a regression guard so clean framework installations cannot produce a false 81-route drift report.
• Starlette 1.3.1 also deprecates the existing httpx TestClient path in favor of httpx2. Kept Starlette on the still-supported >=0.46.0,<1.0.0 line until that test-client migration receives a dedicated compatibility pass; the release gate consequently remains warning-free.
• The cache-free frontend dependency install exposed one high and one moderate Vite/esbuild development-server advisory. Upgraded only the build toolchain to Vite 7.3.6 and React plugin 5.2.0, retained React 18 and MapLibre 4, and declared Node ^20.19.0 || >=22.12.0. npm audit, TypeScript checking and the production build then passed with zero known vulnerabilities.
• A full cache-free Tower image build installed the complete Debian GIS stack, Starlette 0.52.1, FastAPI 0.139.0, PyTorch 2.13.0+cpu, TorchVision 0.28.0+cpu and Ultralytics 8.4.95. GIS import smoke, model-file discovery, PostGIS 3.6, schema/index checks, migration head and browser proxy verification all passed from the newly built image.
• Deployed final release-hardening commit 68ee228 to Tower. The image build used Vite 7.3.6, reported zero npm vulnerabilities, and the all-in-one container became healthy on port 1202. A fresh live QA comparison created quality check 6a0fba57-fae8-4963-95ce-3ba89a3587a4; the browser rendered the corrected single-tile wording, persisted coverage counts and diagnostic separation without console warnings or errors from the Tower host.

Decision

• Sprint 184 is operationally complete. Coverage bias is now explicit and the observed 14-match geometry gap confirms that model quality must not be judged from envelope diagnostics. The next safe model step is an evidence-led footprint-label/matching review, followed by a fresh bounded Mol multi-zone benchmark before any activation or retraining decision.

Sprint 185 - Coverage-aware Mol operational benchmark

Implementation

• Extended the existing real-data quality matrix summaries with the exact persisted tile-coverage population, raw/evaluated/excluded/clipped candidate and reference counts, CRS/tile provenance and diagnostic-only box-to-footprint match gap.
• Preserved manifest-backed Mol municipality, operational-zone, validation split, WGS84 bounds and source reference counts in the multi-sample summary.
• Added build_mol_operational_benchmark_report.py. It groups evidence by the exact model asset, tile size, overlap and confidence threshold and refuses to blend different candidate configurations.
• Added explicit gates for four independent positive Mol holdouts, one pure-empty background control, complete coverage provenance, minimum 90% reference coverage, mean F1 0.25, minimum per-zone F1 0.10 and zero background detections. Envelope matches remain diagnostic and cannot satisfy the canonical F1 gates.
• Wired the report into the persistent Mol operator runner, readiness gate and all-in-one image. The report does not activate, replace, download or train a model and does not change APIs, migrations or inference behavior.

Local validation

• Focused Sprint 126/127/178/184/185 regression set passed: 14 tests.
• Full readiness passed with 512 backend tests, one Alembic head 202606120900, 81 audited API operations, frontend typecheck and Vite 7.3.6 production build.
• npm audit --audit-level=high reported zero vulnerabilities.
• Shell syntax checks passed for the single-, multi-sample and Mol operational runners. Fixture tests prove both an accepted four-zone result and explicit rejection for missing coverage, one-zone collapse and background pressure.

Next pass

• Deploy the operator tooling to Tower and run the current active local model over Achterbos, Gompel, Donk, Postel and the Postel-bos pure-empty control. Record the coverage-aware operational decision before changing model state.

Tower benchmark evidence

• Deployed implementation commit 9f61037, completed the real benchmark and then deployed final evidence/default-gate commit 5db93a9 to the healthy all-in-one runtime with active asset geointel-building-yolov8s-smallbld-minpx3-img640-ft30-pt, tile 512, overlap 64, confidence 0.15 and canonical QA IoU 0.25.
• Canonical results by holdout were: Achterbos F1 0.6694, Gompel 0.6564, Donk 0.5894 and Postel 0.4749. Macro precision/recall/F1 were 0.6296 / 0.5726 / 0.5975; micro precision/recall/F1 were 0.6494 / 0.6189 / 0.6338.
• Across all positive zones, 3,624 of 3,829 raw reference features intersected exact tile coverage. The runner excluded 205 outside features, clipped 60 at boundaries and kept 101 additional envelope matches diagnostic-only.
• Postel-bos produced zero detections. All model, per-zone, coverage provenance, diagnostic-separation and pure-empty-background gates passed. The minimum coverage ratio was 0.9051; the integrity gate was set to 0.90 because OGC bbox retrieval intentionally retains complete edge geometries outside the projected raster polygon.
• The final report under /app/storage/operator-evidence/mol-operational-validation/coverage-aware-20260714T1000Z is accepted with recommendation retain_or_promote_candidate. No model state or weights were changed by the report.
• Exported a four-zone portfolio with 7,078 persisted QA evidence features. Audits found 1,381 canonical false negatives and 1,211 false positives. False-negative medians were about 24-28 m2 and tiny/small buildings dominate; Postel and Donk are the weakest zones.
• Rendered and visually inspected 48 false-negative plus 48 false-positive review cards across every holdout. No source tile was missing. The cards show a mixture of genuine small-building/model errors, temporal GRB/orthophoto differences and box-versus-footprint matching artifacts, so raw FP/FN counts must not be converted directly into new labels.

Decision

• Retain the active model for bounded operator-assisted Mol workflows and demos with QA/QC visible. It is not ground truth and should not be auto-exported as authoritative buildings. Do not start another blind training run; first complete the generated manual review decisions and curate confirmed small- building positives plus confirmed visual hard negatives from the weakest zones.
• Final internal-browser verification loaded the official Mol municipality boundary over the OpenStreetMap road basemap from the persisted PostGIS workspace. The live Tower page reported no console warnings or errors.

Sprint 186 Map-first Mol geographic explorer (2026-07-14)

Changed:

• Made Map the default application workspace and reduced the primary task to data theme, rectangle selection and evidence review.
• Added a dedicated three-column explorer with explicit available/missing themes, full-Mol scope, true MapLibre drag selection, automatic PostGIS queries, exact totals, density, property evidence and GeoJSON handoff.
• Preserved the previous technical workflow behind Geavanceerde werkbank and kept QA, AI and export persistence unchanged.
• Added exact total_feature_count to the vector bbox-selection response while retaining the existing 1,000-feature geometry cap.
• Added provision_mol_context_layers.py for official GRB roads (Wegsegment), water (WTZ, WLAS, WGR) and parcels (ADP) clipped to NIS 13025 and imported through the public dataset API.

Validated:

• Frontend typecheck and production build passed.
• Focused map, orchestration and new explorer tests passed.
• Official fetch-only smoke produced 8,444 Mol road features, 3,668 water features and 32,961 parcels with complete pagination and no truncation.
• Full backend suite passed 517 tests; one legacy component-boundary guard initially failed and was resolved by moving theme API orchestration into useMapThemeSelectionInsights.
• Tower was rebuilt from main; live PostGIS 3.6 connectivity, Alembic head 202606120900, required geometry tables/indexes and browser proxy health all passed.
• The official provisioner persisted 8,444 roads, 3,668 water features and 32,961 parcels alongside the existing 36,941 GRB buildings for the complete Mol municipality.
• In-app browser validation of Volledige gemeente reported 114.55 km2, 36,941 buildings and a building density of 322.5/km2; the bounded preview clearly reported 1,000 of 36,941 features.
• Dataset-detail loading now ignores stale asynchronous responses, preventing a slower municipality-boundary request from overwriting the currently selected building layer or its map context.
• Theme switching reuses the same analyzed rectangle and keeps GeoJSON download/copy actions bound to the active theme result instead of a stale technical dataset selection.

Limitations:

• Population and forest/green remain unavailable rather than simulated until suitable authoritative sources and semantics are selected.

Next:

• Define authoritative population and land-cover source adapters, then reuse the proven municipality provisioner and bbox analysis flow for the complete Kempen.

Sprint 187 Temporal Mol explorer (2026-07-14)

Implemented:

• Added observation/validity/source-version fields to datasets and immutable provenance fields to dataset versions, with one Alembic head and indexed temporal/source identity lookups.
• Persisted dataset version 1 atomically for uploads, demo fixtures and derived vector/raster operations.
• Added source-governed PostGIS selection summaries for object count, area, length, numeric sum and area-weighted sum.
• Added project temporal-series discovery and same-series bbox comparison with honest metric deltas and stable-identity-only object changes.
• Added the map-first Laatste toestand / Evolutie workflow with period selection, automatic rectangle analysis and change overlays.
• Added explicit, idempotent Statbel population (2021-2025) and Digitaal Vlaanderen historical land-use (1778/1873/1969) provisioners for Mol.
• Kept all source fetching operator-triggered; application startup and user queries never fabricate or silently download source data.

Methodology:

• Population totals are source-published per statistical sector. Intersections with partial sectors are labelled area-weighted estimates.
• Historical land-use classes are clipped from official editions and measured in EPSG:31370. They do not claim stable cadastral object identity.
• Every snapshot carries its source URL, observation date, source version, checksum and processing limitations.

Validation before live deployment:

• Script compilation passed.
• New temporal/API/static regression suite passed 6 tests, including append-only/idempotent temporal provenance updates.
• Raster and temporal focused suite passed 27 tests after extending existing assertions to require dataset-version persistence.
• Frontend TypeScript typecheck and production build passed.
• Offline Alembic SQL generation passed with head 202607140001.

Next:

• Run the complete readiness gate, deploy to Tower/PostGIS, provision the official snapshots and verify current/evolution selection end to end in the internal browser.

Live deployment correction:

• The first Tower rollout exposed a deployment race: all-in-one startup and live_migration_smoke.sh both began alembic upgrade head after PostgreSQL became reachable.
• Startup committed head 202607140001; the concurrent smoke transaction rolled back on a duplicate first column. Database contents and the successful migration remained healthy.
• Both Tower deploy entry points now wait for the geointel container healthcheck, which includes completed startup migrations and backend readiness, before launching the independent migration smoke.
• The first Statbel upload exposed 3D sector coordinates (Z=0) against the canonical 2D PostGIS vector column. The source is valid; GeoIntel now preserves the original artifact, records the Z-feature count and explicitly drops Z only for the 2D query index.
• Vector upload persistence is now atomic across Dataset, DatasetVersion and VectorFeature rows, with storage cleanup on rollback. This prevents the failed-indexing orphan state observed during the live import.
• Tower could read the historical WFS capabilities but its gateway rejected XML FILTER content embedded in a GET query. The operator now sends the same read-only WFS 2.0 GetFeature request as XML POST; a live two-feature forest page returned successfully before redeployment.
• The 1778 edition classifies roads as weg, while later editions can use road subclasses. The exact server-side road filter now uses weg*, covering the documented class family without admitting unrelated themes.
• Live population comparison returned 37,015 inhabitants for 2021 and 38,675 for 2025 (+1,660 / +4.48%). Full covered sectors now report exact source totals; only cut sectors are labelled area-weighted estimates.
• Population sector identity is explicitly unstable across annual geometry editions, so source boundary/code changes are not presented as added/removed population objects.

Live operational proof:

• Provisioned the complete Mol context in the canonical municipality project: 36,941 GRB buildings, 8,444 roads, 3,668 water features and 32,961 parcels.
• Provisioned five official Statbel population snapshots (2021-2025) and twelve official historical land-use datasets for buildings, forest, water and roads in 1778, 1873 and 1969.
• The full municipality browser flow reported 114.55 km2, 36,941 buildings, 38,675 inhabitants, 4,203.75 ha historical green/forest class, 3,668 water features, 8,444 roads and 32,961 parcels.
• The full-Mol population comparison reported 37,015 inhabitants in 2021 and 38,675 in 2025 (+1,660 / +4.48%) as an exact whole-sector total.
• A drawn 29.99 km2 northern-Mol rectangle returned 540 buildings, an area-weighted estimate of 152 inhabitants, 1,365.74 ha historical green/forest class, 624 water features, 732 roads and 1,715 parcels. Its 2021-2025 population estimate changed from 151 to 152 and was explicitly labelled Ruimtelijke schatting.
• Internal-browser checks at 1280x720 and 3440x1440 confirmed no horizontal overflow. A ResizeObserver plus workbench-scoped absolute map fill keeps the MapLibre canvas equal to its GIS stage after responsive layout changes.
• The final deployed runtime passed container health, PostGIS 3.6 connectivity, required schema/index checks, Alembic head 202607140001, frontend proxy verification and browser console inspection with no warnings or errors.

Remaining source limitations:

• The latest official population snapshot in this workspace is 2025.
• The forest/green evolution series currently represents the available historical land-use editions through 1969; it must not be presented as current forest cover.
• Partial-sector population results remain area-weighted estimates because no finer authoritative population surface has been ingested.

Sprint 188 Official modern Mol land-use series (2026-07-14)

Implemented:

• Added provision_official_landuse_timeseries.py for explicit MercatorNet WCS subsets of the official Departement Omgeving version 3 land-use maps for 2013, 2016, 2019, 2022 and 2025.
• Validated one-band integer GeoTIFF input, EPSG:31370, 10 m resolution and the documented 1-19 class domain before processing.
• Preserved every raw raster, exact request URL, catalogue URL, raster/vector checksum, class histogram and processing manifest in operator storage.
• Polygonized documented class 12 (Bos) in metric CRS, clipped it to the official Mol boundary, normalized it to EPSG:4326 and uploaded it through the existing API/DatasetService/vector-feature path.
• Added source-governed hectare aggregation metadata, 10 m/non-cadastral limitations and unstable raster-polygon identity declarations.
• Kept department-omgeving:land-use:forest:mol separate from the historical land-use series and added a compact frontend series selector.

Source validation:

• Live WCS capabilities exposed all five expected coverages through lu:lu_landgebruik_vlaa_<year>_v3.
• A full-Mol fetch-only run produced 3,768 / 3,823 / 3,953 / 3,615 / 3,676 forest polygons for 2013 / 2016 / 2019 / 2022 / 2025 without truncation.
• Polygonized forest area was 3,723.17 / 3,615.39 / 3,592.27 / 3,648.98 / 3,626.56 ha. These are measurements within the official 10 m representation, not cadastral forest areas.
• Every raster cell touching the municipality is considered before exact vector clipping. The 916 NoData edge cells in each WCS subset were explicitly excluded and recorded rather than assigned a class.

Validation:

• The first live import was rejected before any upload because the operator requested 500 datasets while the canonical API limit is 200. The operator now follows every 200-item page and a focused pagination regression test covers 405 records.
• New focused backend suite passed 5 tests.
• Full readiness passed 533 backend tests, one Alembic head, frontend TypeScript typecheck/build and all repository smoke gates.
• Tower rebuilt successfully and passed PostGIS 3.6, migration, container health and browser-proxy validation.
• All five modern forest snapshots were persisted in the canonical Mol project: 3,768 / 3,823 / 3,953 / 3,615 / 3,676 vector features for 2013 / 2016 / 2019 / 2022 / 2025. A second operator run reused every existing dataset without uploading duplicates.
• The live full-Mol comparison returned 3,723.17 ha for 2013 and 3,626.56 ha for 2025, a measured change of -96.60 ha (-2.60%) within the documented 10 m raster representation.
• Internal-browser verification confirmed latest-state source selection, separate modern and historical series, exact warning text, valid MapLibre rendering and an empty warning/error console.
• A final presentation correction aligned the active MapLibre and legend colors with each data theme and removed the old 105rem content cap only for the primary explorer on ultrawide screens.
• Deployed presentation commit 3a2d1fa to Tower. The all-in-one container became healthy, live PostGIS/migration/schema validation passed and the browser proxy remained reachable on port 1202.
• Final in-app browser measurements showed no horizontal overflow at 1280x720 or 3440x1440. At 3440 px the explorer uses 3,134 px and its MapLibre stage 2,408 px; forest layer/legend colors resolve to the green theme and the console contains zero warnings or errors.

Next:

• Define the exact municipality list or administrative boundary that GeoIntel will call Kempen, then reuse the proven source operators and temporal query path for that scope without changing Mol semantics.

Sprint 189 Official Kempen operational scope (2026-07-14)

Implemented:

• Chose the official Vlaamse Vervoerregio Kempen as GeoIntel's reproducible operational regional scope. It has 28 municipalities and explicitly includes both Mol and Nijlen.
• Added geographic_scopes.py as the operator registry for scope identity, current municipality names/NIS codes, policy authority and limitation text.
• Added provision_geographic_scope.py to fetch current authoritative VRBG boundaries, validate all members, create a valid regional union and retain boundary/member GeoJSON plus checksums and manifest.
• The operator creates or reuses Kempen Regional Workbench, one regional Area, 28 municipality Areas and two source datasets exclusively through canonical API envelopes.
• Added a compact Mol/Kempen selector to the primary map flow, scope-aware heading text and the neutral Volledig werkgebied action.
• Kept thematic regional fetching outside the scope pass. No startup fetch, direct PostGIS write, fake metric or unbounded regional theme download was introduced.

Source proof:

• A live read-only VRBG run matched all 28 registered municipalities and current NIS codes.
• The valid union measured 1,399.2505 km2 in EPSG:31370 with WGS84 bbox [4.59723873, 51.01047967, 5.26224853, 51.50511313].
• The scope is labelled as the transport-policy region and never as the complete cultural, landscape or historical Kempen.

Validation before deployment:

• New focused scope and map-flow suite passed 9 tests.
• Python scope/operator compilation and frontend TypeScript typecheck passed.

Deployment target:

• Deploy and run the scope operator on Tower, verify 29 Areas and both boundary datasets, then design bounded regional theme partitions before importing high-volume GRB or WCS data.

Live operational proof:

• Deployed scope foundation commit bb7310e and context-isolation/copy corrections through 94f5e01 to the all-in-one Tower runtime on port 1202.
• Provisioned Kempen Regional Workbench through the canonical API with one regional Area, 28 municipality Areas and two VRBG datasets. Repeating the operator returned the same project, Area and dataset identifiers.
• The live project contains 29 Areas, one valid regional-boundary feature and 28 municipality-boundary features with persisted authority, policy-scope limitation, source URL and checksums.
• A live Mol -> Kempen switch now clears all project-bound state, rejects stale project-data responses, selects Vervoerregio Kempen - officiële operationele grens and opens kempen_transport_region_boundary_2026-07-14.geojson without retaining a Mol dataset.
• The reverse Kempen -> Mol switch restored Gemeente Mol - officiële grens, the GRB building layer and all six available Mol themes.
• Internal-browser checks passed at 1280x720 and 3440x1440. The ultrawide explorer used 3,134 px, its MapLibre canvas used 2,408 px and no horizontal overflow or browser warning/error was present.
• Full readiness passed 538 backend tests, one Alembic head, frontend typecheck/build and every repository smoke gate. Tower additionally passed container health, PostGIS 3.6, migration/schema/index and frontend proxy checks.

Next:

• Build bounded, idempotent regional theme ingestion in municipality-sized partitions, starting with current buildings and retaining per-member provenance before exposing any Kempen-wide metric in the explorer.

Sprint 190 Regional Kempen GRB buildings (2026-07-14)

Implemented:

• Added provision_regional_grb_buildings.py for the approved 28-member scope, with complete OGC pagination, per-member safety caps, resumable checksummed artifacts and an explicit total cap.
• Added deterministic cross-boundary ownership by maximum member intersection and NIS-code tie breaking, preventing duplicate source identities without cutting the final geometry at internal municipality borders.
• Added streaming managed-artifact copy plus batch-wise partition indexing through DatasetService and VectorFeatureService. One regional Dataset remains compatible with existing viewport, selection, QA and export ownership boundaries.
• Added exact manifest/index count enforcement, duplicate source-feature rejection and rollback/storage cleanup on import failure.
• Kept fetching operator-triggered and local service import container-only. No startup fetch, browser provider fetch, direct vector-feature SQL or public API change was introduced.

Validation before deployment:

• Backend/application and operator compilation passed.
• New focused operator/storage/batch-index suite passed 5 tests.
• Dataset, vector persistence, Mol municipality and Kempen scope regression selection passed 37 tests.

Next:

• Run the complete readiness gate, deploy to Tower, execute the full 28-partition source job, verify exact PostGIS counts and perform full-region plus rectangle browser selections.