Record AOI512 YOLO candidate gate

CHANGELOG.md

@@ -7,6 +7,15 @@
 
 # Changelog
 
+## Sprint 147 AOI512 YOLOv8s scale-match candidate gate (2026-07-09)
+
+- Built and audited an AOI-scale YOLO dataset at `512px` tile size to test whether the previous `160px` training scale was the main quality blocker.
+- Trained Tower-local model asset `geointel-building-yolov8s-aoi512e80-pt` from `/app/storage/operator-data/yolo-building-aoi512-uniquehardneg`.
+- Ran 7 positive AOI sweeps, a 17,156-feature evidence portfolio, a 9-sample hard-negative/background matrix and a promotion report.
+- Result: the candidate is rejected. The best threshold `0.25` reached mean positive F1 `0.13511851520077328` and still produced max background detections `56`.
+- Conclusion: scale-match training helps the training validation curve but does not solve operational persisted QA/QC quality. The next model pass needs better positive AOI coverage and label strategy, not only more epochs or another threshold.
+- No API contract, migration, frontend behavior, provider fetching, model download or active model configuration changed.
+
 ## Sprint 146 Unique hard-negative YOLOv8s candidate gate (2026-07-09)
 
 - Fixed the all-in-one Docker image so the operator YOLO training wrapper is available at `/app/scripts/train_operator_yolo_detector.sh`.

docs/CODEX_EXECUTION_LOG.md

@@ -1,3 +1,61 @@
+## 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:

docs/TODO.md

@@ -109,8 +109,10 @@ This file now starts with the current implementation status. Older preparation/b
 - [x] Train a hard-negative-balanced YOLO candidate and rerun dense QA plus background false-positive matrices.
 - [x] Benchmark an external remote-sensing YOLOv8l building candidate as an explicit local model asset.
 - [x] Train and gate the `uniquehardneg160e50` YOLOv8s candidate through 7 positive AOIs and 9 hard-negative/background samples.
+- [x] Train and gate an AOI-scale `aoi512e80` YOLOv8s candidate to test the 160px training-scale hypothesis.
 - [ ] Find or train a materially stronger aerial/Kempen building model candidate; `geointel-building-yolov8n-expanded160e50-pt` is the best current dense-AOI candidate but still too weak and too noisy for a V1 default.
 - [ ] Train a higher-capacity local aerial-building detector with stronger positive recall while preserving the hard-negative false-positive gate.
+- [ ] Add more diverse positive AOIs and revisit geometry-to-box label strategy before the next default-model training attempt.
 
 ## Sprint 8 status