# GeoIntel Backend (Sprint 3 foundation layer)

FastAPI backend for GeoIntel Kempen Foundation Sprints.

The map-first explorer uses the existing persisted vector selection endpoint. Its bounded GeoJSON preview reports `feature_count`, while `total_feature_count` reports the exact PostGIS intersection count before the 1,000-feature response cap. This keeps municipality-scale analysis honest without sending unbounded geometry to the browser.

## Scope implemented
- Project CRUD
- Area CRUD with PostGIS geometry
- Vector and raster dataset upload/registration
- Deterministic local storage metadata capture
- PostGIS migration and database foundation
- Job foundation for async-ready GIS operations

## Sprint 2 additions
- Dataset typing and lifecycle support:
  - `uploaded`
  - `validating`
  - `ready`
  - `failed`
- Vector metadata extraction:
  - feature count
  - geometry type summary
  - bounds
  - approximate area
  - CRS and CRS assumption
- Raster metadata endpoint:
  - returns raster profile when `rasterio` is available
  - returns clear `RASTER_PROCESSING_UNAVAILABLE` error when dependency is missing
- Deterministic storage metadata capture:
  - original filename
  - stored filename
  - MIME/content type
  - size bytes
  - checksum SHA-256

## Sprint 3 additions
- Lightweight job architecture:
  - `jobs` table and migrations
  - job create/list/read/status API
  - synchronous execution behind job abstraction
- Vector operations foundation:
  - inspect
  - bbox
  - stats
  - clip by area
  - buffer
  - intersect
  - invalid geometry rejection with typed errors
- Raster operation foundation:
  - inspect
  - metadata
  - preview readiness
  - clip by area (dependency-aware with unavailable fallback)
  - tile generation with manifest output
  - real preview image generation when dependencies are installed

## Sprint 4 additions
- Raster foundation is now implemented with real extraction and deterministic artifact outputs:
  - metadata returns width, height, band count, CRS, bounds, resolution, dtype, nodata, transform
  - preview endpoint generates and reuses PNG previews with width/height
  - clip operation persists a derived raster dataset with:
    - `source_dataset_id`
    - `operation`
    - `operation_parameters`
  - tile operation writes deterministic raster tiles under `tiles/{project_id}/{source_dataset_id}/{tile_set_id}`
  - tile manifest includes tile path, pixel window, bounds, transform, and count
- Dependency behavior:
  - when `rasterio` is missing, raster processing returns `RASTER_PROCESSING_UNAVAILABLE`
  - preview endpoint additionally requires numpy/pillow and returns `RASTER_PROCESSING_UNAVAILABLE` when missing

## Sprint 5 additions
- Raster analytics hardening:
  - raster band statistics now include:
    - min, max, mean, std
    - nodata count and ratio
    - valid pixel count
    - dtype
    - optional histogram bins (default 16 bins)
  - raster reproject operation implemented (CRS transform + rasterio reprojection) using dependency-aware raster processing checks.
  - reproject failures are explicit (`INVALID_PARAMETERS`, `INVALID_DATASET_CRS`, `RASTER_PROCESSING_UNAVAILABLE`).
- Raster clip and tile hardening:
  - clip validates area presence and CRS alignment constraints.
  - tile manifest records `tile_set_id`, `tile_size`, `overlap`, `source_dataset_id`, `source_raster_id`, bounds, parameters, count, tile paths, `ai_inference`, and `tile_server`.
- Job result persistence for raster ops:
  - raster clip/reproject/tile job payloads persist derived dataset references when outputs are produced.

## Sprint 6 additions
- Added local spectral index operations:
  - NDVI endpoint: `POST /raster/indices/ndvi`
  - NDWI endpoint: `POST /raster/indices/ndwi`
  - NDBI endpoint: `POST /raster/indices/ndbi`
- Spectral index input validation:
  - band parameters must be positive integers
  - band parameters must exist in source raster band count
- Dependency-aware execution:
  - returns `RASTER_PROCESSING_UNAVAILABLE` when rasterio or numpy are unavailable
- Real index output handling:
  - local windowed float32 GeoTIFF generation
  - `NaN` strategy for invalid pixels / division by zero
- Provenance capture for derived index datasets:
  - `source_dataset_id`, `operation`, `band_mapping`, `formula`
  - `output_dtype`, `nodata_strategy`, `value_range_note`
  - `output_dataset_id`, `created_at`, `path`

## Sprint 7B additions
- Added provider registry skeleton for `grb`, `osm`, `manual` and `fixture`.
- Added provider capability endpoints:
  - `GET /api/v1/external/providers`
  - `GET /api/v1/external/providers/{provider_name}`
  - `GET /api/v1/external/providers/{provider_name}/layers`
  - `GET /api/v1/external/providers/{provider_name}/status`
  - `POST /api/v1/external/providers/{provider_name}/import`
- GRB and OSM imports return explicit `not_configured` responses; no live WFS or Overpass calls are made.
- Manual and fixture providers describe existing upload/fixture flows only.
- Added live PostGIS migration smoke script for environments with a real database:

```bash
bash scripts/live_migration_smoke.sh
```

## Sprint 8 additions
- Added Detection Lab foundation:
  - `detections` ORM model and Alembic migration with PostGIS geometry storage.
  - hardened `analysis_runs` for dataset/job/model/result metadata.
  - model registry capability service for `yolo-placeholder` and `manual-fixture-detector`.
  - detection service boundary for creating jobs, analysis runs and dependency-aware unavailable responses.
- Added detection endpoints:
  - `GET /api/v1/detection/models`
  - `GET /api/v1/detection/model-assets`
  - `POST /api/v1/detection/run`
  - `GET /api/v1/detection/runs/{analysis_run_id}`
  - `GET /api/v1/detection/runs/{analysis_run_id}/detections`
- YOLO/PyTorch real inference is not enabled in Sprint 8.
- Fixture detector mode is test/demo-only and requires explicit `fixture_mode=true`.

## Sprint 8B additions
- Added optional configured YOLO integration foundation:
  - `yolo-configured` model registry capability.
  - import-safe adapter for local Ultralytics model files.
  - raster tile manifest validation and tile limit enforcement.
  - pixel bbox to EPSG:4326 detection polygon conversion.
  - persisted detections through the existing detection/job/analysis-run path.
- YOLO dependencies are optional extras and are not required for backend startup.
- GeoIntel does not download YOLO model weights automatically.

## Sprint 8C additions
- Added detection visualization/review API support:
  - list detection runs
  - list detections by run or dataset with class/confidence filters
  - get detection detail
  - return persisted detections as GeoJSON FeatureCollections
- Added detection QA against reference vector datasets:
  - compares persisted detection geometries against persisted `vector_features`
  - persists `quality_checks` and `metrics`
  - returns precision, recall, F1, mean IoU and false positive/negative counts
  - configured-YOLO runs clip both QA populations to persisted tile-manifest
    coverage before matching and fail closed on missing/mismatched coverage
    provenance
  - persists a diagnostic-only candidate-box versus reference-envelope pass so
    box-to-footprint matching artifacts are visible without altering canonical
    footprint-IoU metrics
- Segmentation, LiDAR, AI Copilot, Training Studio and Reports remain out of scope.

## Sprint 9 additions
- Added Segmentation Lab foundation:
  - `segmentations` ORM model and Alembic migration with PostGIS MultiPolygon geometry storage.
  - segmentation model registry capabilities for `segmentation-placeholder`, `fixture-segmenter`, `yolo-seg-configured` and `sam-configured`.
  - segmentation service boundary for creating jobs, analysis runs and unavailable model responses.
  - explicit fixture segmenter mode for tests/demo fixtures only.
- Added segmentation endpoints:
  - `GET /api/v1/segmentation/models`
  - `POST /api/v1/segmentation/run`
  - `GET /api/v1/segmentation/runs`
  - `GET /api/v1/segmentation/runs/{analysis_run_id}`
  - `GET /api/v1/segmentation/runs/{analysis_run_id}/segmentations`
  - `GET /api/v1/segmentation/runs/{analysis_run_id}/geojson`
  - `POST /api/v1/segmentation/runs/{analysis_run_id}/qa/reference`
- Real SAM and YOLO-seg inference are not enabled in Sprint 9.
- Mask paths are provenance/debug artifacts; persisted PostGIS geometry is authoritative for QA, map display and GeoJSON.

## Sprint 17 additions
- Added export foundation backed by the existing `exports` table.
- GeoJSON exports now persist export records and write JSON artifacts for:
  - vector datasets
  - detection analysis runs
  - segmentation analysis runs
- Added project metadata JSON export for project, dataset and QA/QC summary state.
- Added export read/list/content endpoints:
  - `POST /api/v1/exports/geojson`
  - `POST /api/v1/exports/metadata`
  - `GET /api/v1/exports/projects/{project_id}/exports`
  - `GET /api/v1/exports/{export_id}`
  - `GET /api/v1/exports/{export_id}/content`
- Exported detection and segmentation GeoJSON is generated from persisted first-class geometry rows.
- No new migrations, product lines, live providers or AI dependencies are introduced by this export pass.
- Old offline demo export artifacts can be inspected with `python scripts/cleanup_demo_artifacts.py`
  and removed only with an explicit `--apply`. The script keeps the newest exports
  per demo project, refuses to delete files outside `STORAGE_ROOT`, and blocks
  apply runs above `--max-delete` until the cap is raised after a dry-run review.
  Use repeated `--export-type` values to target only specific artifact kinds.
  In Docker, use `docker compose exec -T backend python scripts/cleanup_demo_artifacts.py`.
- Live cleanup validation is available with `bash scripts/verify_demo_cleanup_dry_run.sh`.
  It runs the same maintenance path without `--apply` and fails if the summary
  reports anything other than a dry-run with zero deleted exports/files.

## Run locally

### Prerequisites

- Python 3.11+
- PostgreSQL with PostGIS

### Install dependencies

```bash
cd backend
python -m pip install -e .[dev]
```

Optional AI dependencies for configured local YOLO inference:

```bash
cd backend
python -m pip install -e .[ai]
```

Docker and Unraid builds keep AI dependencies disabled by default. To build an
image with local PyTorch/Ultralytics support, set:

```bash
GEOINTEL_INSTALL_AI=true
```

The default remains `false` so normal GIS deployments do not install the large AI
runtime. GeoIntel still requires an explicit local model path and never downloads
weights automatically.

AI-enabled Docker images include the native OpenCV runtime libraries required by
Ultralytics. Dependency availability is checked with real `torch` and
`ultralytics` imports, so missing shared libraries are reported as
`dependency_unavailable` instead of being treated as configured.
Docker/Unraid runtimes set `YOLO_CONFIG_DIR` to a writable storage path so
Ultralytics does not attempt to write settings under the root user config
directory.

Configured YOLO requires:

```bash
YOLO_ENABLED=true
YOLO_MODELS_DIR=/absolute/path/to/models
YOLO_MODEL_PATH=/absolute/path/to/local-model.pt
```

Optional local model compatibility smoke:

```bash
python scripts/yolo_preflight.py --model-path /absolute/path/to/local-model.pt --tile-manifest-path /absolute/path/to/manifest.json --check-model-load --json
```

In Docker, run the same smoke through the backend container:

```bash
docker compose exec -T backend python scripts/yolo_preflight.py --model-path /absolute/path/to/local-model.pt --tile-manifest-path /absolute/path/to/manifest.json --check-model-load --json
```

In the all-in-one Unraid runtime, place model files under the configured models
directory, mounted as `/app/models` by default:

```bash
GEOINTEL_MODELS_PATH=/mnt/user/appdata/geointel/models
YOLO_ENABLED=true
YOLO_MODELS_DIR=/app/models
YOLO_MODEL_PATH=/app/models/local-model.pt
```

The root helper can write those values safely after a local model is placed:

```bash
python scripts/configure_yolo_model.py \
  --models-dir /mnt/user/appdata/geointel/models \
  --env-file /mnt/user/appdata/geointel/.env \
  --apply
```

When a promotion report recommends an exact model/tile/threshold candidate,
prefer the guarded activation helper. It validates the report, checks the local
model asset and writes `.env` only when `--apply` is supplied:

```bash
python scripts/activate_promoted_yolo_candidate.py \
  --promotion-report /mnt/user/appdata/geointel/artifacts/detection-model-promotion/split-aware/aoi1024bg512r3e50-high-threshold-split-20260710T222934Z/detection_model_promotion_report.json \
  --candidate-key 'geointel-building-yolov8s-aoi1024bg512r3e50-pt|512|64|0.35' \
  --models-dir /mnt/user/appdata/geointel/models \
  --env-file /mnt/user/appdata/geointel/.env \
  --json
```

Add `--apply` only after reviewing the emitted env updates. The smoke and
activation helpers load no model by default, run no inference and do not
download weights. Restart or rebuild the runtime after applying because the
active model is read from `YOLO_MODEL_PATH`.

Operator-only local training preparation is available when real public model
candidates are too weak for the target imagery. It is not a browser feature and
does not change API contracts:

```bash
docker exec -it geointel python3 /app/scripts/export_operator_yolo_dataset.py \
  --manifest-path /app/storage/operator-data/operator_samples_manifest.json \
  --output-dir /app/storage/operator-data/yolo-building-dataset \
  --val-samples turnhout \
  --force
```

In an AI-enabled runtime with an existing local base model:

```bash
docker exec \
  -e OPERATOR_YOLO_DATASET_DIR=/app/storage/operator-data/yolo-building-dataset \
  -e YOLO_BASE_MODEL_PATH=/app/models/yolov8n.pt \
  -e TRAIN_MODEL_OUTPUT_PATH=/app/models/geointel-building-detector.pt \
  -e TRAIN_EPOCHS=8 \
  -e TRAIN_IMGSZ=512 \
  -e TRAIN_BATCH=2 \
  -e TRAIN_WORKERS=0 \
  -e TRAIN_DEVICE=cpu \
  -e PYTHON_BIN=python3 \
  geointel bash /app/scripts/train_operator_yolo_detector.sh
```

The exporter creates a YOLO `dataset.yaml` plus image/label folders from the
explicit operator sample manifest. The training wrapper writes
`training_summary.json` and a local `.pt` artifact, which still must be
validated through model preflight and the real-data QA matrix before use.

For a larger tile-level training set, use overlapping windows instead of one
image per AOI:

```bash
docker exec -it geointel python3 /app/scripts/export_operator_yolo_tile_dataset.py \
  --manifest-path /app/storage/operator-data/operator_samples_manifest.json \
  --output-dir /app/storage/operator-data/yolo-building-tile-dataset \
  --tile-size 192 \
  --stride 96 \
  --negative-keep-ratio 0.5 \
  --val-samples turnhout \
  --force
```

Then point `OPERATOR_YOLO_DATASET_DIR` at
`/app/storage/operator-data/yolo-building-tile-dataset` and keep the same
training wrapper. Tile-level output remains operator tooling outside the V1
browser product.

Use `--samples` (or `OPERATOR_YOLO_SAMPLES`) when an experiment needs a
deliberate manifest subset. The generated summary records the source manifest
count plus selected and excluded sample slugs. Unknown samples and any selected
manifest holdout that is omitted from `--val-samples` fail before files are
written.

The backend also exposes a read-only model asset catalog for the mounted model
directory:

```bash
curl http://localhost:1202/api/v1/detection/model-assets
```

The catalog lists local `.pt`, `.onnx` and `.engine` files with size, SHA-256
and active-model status. Detection runs may submit `model_asset_id` with
`model_id="yolo-configured"` to use a cataloged local model for that run. The
backend resolves the ID to a file inside `YOLO_MODELS_DIR`; browser clients do
not send arbitrary model paths.

Configured YOLO inference uses raster tile artifacts from the existing tile
manifest flow. Single-band or otherwise non-RGB tile images are converted to a
temporary RGB prediction image before inference; georeferencing still comes
from the persisted tile manifest transform/bounds metadata.

Optional tuning:

```bash
YOLO_MODEL_ID=yolo-configured
YOLO_MODEL_DISPLAY_NAME="Configured YOLO detector"
YOLO_MODEL_VERSION=local-v1
YOLO_MODELS_DIR=/app/models
YOLO_CONFIG_DIR=/app/storage/ultralytics
YOLO_DEVICE=cpu
YOLO_IMAGE_SIZE=640
YOLO_MAX_TILES=100
YOLO_MAX_DETECTIONS=1000
YOLO_DUPLICATE_IOU_THRESHOLD=0.5
YOLO_BATCH_SIZE=1
```

### YOLO local preflight

Sprint 13 adds a local-only preflight for configured YOLO paths:

```bash
python scripts/yolo_preflight.py --model-path /absolute/path/to/local-model.pt --tile-manifest-path /absolute/path/to/manifest.json
```

Machine-readable output:

```bash
python scripts/yolo_preflight.py --model-path /absolute/path/to/local-model.pt --tile-manifest-path /absolute/path/to/manifest.json --json
```

To validate only local model/manifest paths on a machine without optional AI dependencies:

```bash
python scripts/yolo_preflight.py --model-path /absolute/path/to/local-model.pt --tile-manifest-path /absolute/path/to/manifest.json --assume-dependencies --json
```

The preflight checks configuration, dependency availability, local model file existence, tile manifest validity, tile count and referenced tile paths. JSON output also includes runtime diagnostics for the model directory, `YOLO_CONFIG_DIR`, installed `torch`/`ultralytics` versions and CUDA availability when dependency checks pass. It does not load a YOLO model, run inference or download weights.

`YOLO_MAX_DETECTIONS` is forwarded to Ultralytics as `max_det`. The default is
`1000` because dense building AOIs can exceed the upstream default cap of 300
detections before QA/QC can measure recall honestly.

`YOLO_DUPLICATE_IOU_THRESHOLD` controls GeoIntel-side cross-tile duplicate
suppression after YOLO pixel boxes are converted to EPSG:4326 polygons and
before `Detection` rows are persisted. Candidates are sorted by confidence per
class; lower-confidence same-class candidates with geometry IoU greater than or
equal to the threshold are suppressed. The default is `0.5`; set `0` to disable
this post-processing for debugging.

The same read-only status is available through the API and Detection Lab UI:

```bash
curl http://localhost:1202/api/v1/detection/yolo/preflight
```

To validate the full configured-YOLO runtime path against Docker/Tower after a
model is mounted and selected, run:

```bash
bash scripts/verify_model_asset_detection_workflow.sh http://192.168.10.150:1202
```

The smoke uses the existing demo raster to generate a tile manifest, selects a
cataloged local model asset, verifies read-only preflight, submits the existing
detection run endpoint and checks persisted AnalysisRun, Detection list and
Detection GeoJSON output. It does not download weights or inject detector
fixtures. A zero detection result is still a valid runtime smoke outcome on the
synthetic demo raster.

To validate the configured building model on operator-provided GIS data, mount
or copy a real georeferenced raster and a real reference-building GeoJSON onto
the runtime host, then run:

```bash
REAL_RASTER_PATH=/mnt/user/appdata/geointel/data/orthophoto.tif \
REAL_REFERENCE_VECTOR_PATH=/mnt/user/appdata/geointel/data/reference-buildings.geojson \
bash scripts/verify_real_data_detection_qa_workflow.sh http://192.168.10.150:1202
```

This smoke refuses missing/unsupported inputs, uploads the raster and reference
dataset through the normal dataset service, generates raster tiles, selects a
local model asset, runs configured YOLO detection, compares persisted
detections against persisted `vector_features`, persists QA/QC rows and exports
the detection GeoJSON. It never seeds demo detections, enables fixture mode,
fetches live providers or downloads model weights. Configured-YOLO model class
labels are normalized to lowercase for filtering and persisted detections while
the original model label is retained in detection provenance. Raster tile
manifests generated for AI handoff include source CRS metadata so pixel-space
model outputs can be transformed to WGS84 GeoJSON coordinates. Current V1 upload
support is limited to GeoTIFF-style rasters and GeoJSON/JSON reference vectors.

When `REAL_AREA_BBOX=minx,miny,maxx,maxy` is supplied, the same workflow also
persists an EPSG:4326 project Area before uploading data. `REAL_AREA_NAME` and
`REAL_PROJECT_REGION` retain operator context. The multi-sample runner fills
these values from manifest `wgs84_bbox` and municipality metadata, so generated
projects are immediately usable in the map without an alternate persistence
path or API contract.

To prepare the documented operator sample corpus inside the all-in-one runtime
container, run:

```bash
docker exec -it geointel python3 /app/scripts/prepare_operator_real_data_samples.py
```

The helper writes GeoTIFF orthophotos, GRB GBG building GeoJSON files and
`operator_samples_manifest.json` under `/app/storage/operator-data`. In
addition to the established positive and background AOIs, the registry contains
Beerse, Rijkevorsel, Hoogstraten and Vorselaar as focused small-building
training AOIs. Vosselaar and Grobbendonk are independent validation AOIs and
must not be exported into the training split. Background candidates can persist
empty GRB FeatureCollections for negative-tile training; normal reference AOIs
still fail when GRB returns no buildings. These are runtime artifacts only and
are not committed to Git.

Mol additionally has operational holdouts for Achterbos, Gompel, Donk and
Postel, with Mol center as the historical baseline and Postel-bos as a separate
background control. Prepare and execute that pack with the documented
`prepare_operator_real_data_samples.py` and
`run_mol_operational_validation.sh` commands in `scripts/README.md`. The runner
produces a coverage-aware operational decision report: canonical footprint-IoU
metrics remain authoritative, reference-envelope matches remain diagnostic,
and no report can activate or mutate a model asset.

For municipality-wide navigation, run
`/app/scripts/provision_mol_municipality_workspace.py` inside the all-in-one
container. It verifies the official Mol boundary (NIS `13025`), pages and clips
all GRB GBG buildings, records checksums/provenance under persistent operator
storage and imports both datasets through the existing HTTP service boundary.
The command is explicit and idempotent; it is never executed during backend
startup. See `scripts/README.md` for exact usage and refresh controls.

The current recommended local building model is
`geointel-building-yolov8s-smallbld-minpx3-img640-ft30-pt` with tile size
`512`, overlap `64` and confidence threshold `0.15`. Its SHA256 is
`a9088b8491dfae36694b53e9e9406cb4e3511d334a5712fa34f75078a47759c1`.
The promotion evidence covers seven positive AOIs at QA match IoU `0.25` and
three pure-empty background AOIs. The model improves recall and persisted
false-negative counts, but has lower precision than the previous balanced
model; operators must review and persist QA/QC rather than treating detections
as ground truth.

For model-quality calibration, run the confidence sweep wrapper:

```bash
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 \
CALIBRATION_THRESHOLDS="0.50 0.35 0.25 0.15" \
bash scripts/run_detection_calibration_sweep.sh http://192.168.10.150:1202
```

The sweep creates one real persisted workflow run per threshold, fetches the
persisted `QualityCheck`/`Metric` rows and writes a `calibration_summary.json`
with persisted detection count, raw candidate count, suppressed duplicate count,
duplicate IoU threshold, score, precision, recall, F1, mean IoU and false
positive/negative counts. It is intended to tune confidence/IoU/model choices,
not to add new inference behavior.

To compare local model assets and tile settings as well as thresholds, run the
quality matrix wrapper:

```bash
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 \
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
```

The matrix creates one real persisted workflow run per combination and writes
`quality_matrix_summary.json` with the selected model asset, tile size, tile
overlap, threshold, detection count, QA score, precision, recall, F1, mean IoU
and false-positive/false-negative counts. It ranks `best_by_score`,
`best_by_recall` and `best_by_precision`. It does not download weights, create
fake detections, fetch live providers or change backend API behavior.

To aggregate the same matrix over every prepared operator sample, run:

```bash
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
```

The combined `multi_sample_quality_summary.json` reports per-sample and overall
best configurations. It is an operator benchmarking command, not a backend API
or provider import path.

Before promoting any local model as a default, also run the hard-negative
matrix against the documented background candidates:

```bash
OPERATOR_SAMPLE_MANIFEST_PATH=storage/operator-data/operator_samples_manifest.json \
OPERATOR_BACKGROUND_SAMPLE_SLUGS="postel_bos lommel_heide kasterlee_bos" \
QUALITY_MODEL_ASSET_IDS="geointel-building-yolov8n-expanded160e50-pt geointel-building-yolov8n-tile30-pt yolov8s-building-segmentation-pt" \
QUALITY_TILE_SIZES="640" \
QUALITY_TILE_OVERLAPS="64" \
QUALITY_THRESHOLDS="0.25 0.15 0.05" \
bash scripts/run_operator_hard_negative_detection_matrix.sh http://192.168.10.150:1202
```

This path uploads only background rasters, runs configured-YOLO detection and
counts detections as false-positive pressure. It does not upload reference
vectors or run QA/QC, so it cannot produce fake precision/recall metrics for
empty background AOIs.

To inspect the evidence behind a calibration run, export the persisted QA
evidence bundle:

```bash
CALIBRATION_SUMMARY_PATH=/mnt/user/appdata/geointel/artifacts/detection-calibration/20260707T002103Z/calibration_summary.json \
bash scripts/export_detection_calibration_evidence.sh http://192.168.10.150:1202
```

The bundle writes combined QA evidence GeoJSON plus a standalone HTML/SVG review
artifact that separates matched detections, matched references, false positives
and false negatives by role. It reads existing persisted `QualityCheck` evidence
only and does not rerun inference.

For source-image review of false negatives, run
`scripts/render_detection_false_negative_review_contact_sheets.py` against a
fixed-threshold evidence portfolio. It uses the selected run's persisted tile
manifest, overlays candidate/reference context and explicitly exports reference
features outside tile coverage. The command is read-only and never changes
`QualityCheck`, `Metric`, `Detection` or model state.

### Run backend

```bash
cd backend
python -m uvicorn app.main:app --reload
```

### Run backend tests

```bash
cd backend
python -m pytest
```

For warning-sensitive release checks, the backend is expected to pass with Python deprecation warnings promoted to errors for the timestamp-heavy service paths:

```bash
cd backend
python -m pytest -W error::DeprecationWarning tests/test_geojson_dataset_service.py tests/test_qa_service.py tests/test_sprint7a_persistence_foundation.py tests/test_sprint8c_detection_visualization_qa.py tests/test_sprint9_segmentation_foundation.py tests/test_vector_operations_service.py
```

The repository readiness gate now applies the same warning policy to the full backend suite:

```bash
bash scripts/run_readiness_check.sh
```

That readiness gate also runs the API contract smoke check before backend/frontend compilation and tests.

### Golden QA/QC benchmark

Sprint 12 includes a deterministic QA/QC regression benchmark using explicit fixture data:

```bash
python scripts/run_golden_qa_benchmark.py
```

Machine-readable output:

```bash
python scripts/run_golden_qa_benchmark.py --json
```

Shell wrapper used by release-readiness checks:

```bash
bash scripts/verify_golden_qa_benchmark.sh
```

The benchmark compares `fixtures/golden/predicted_buildings.geojson` against `fixtures/golden/reference_buildings.geojson` and fails on metric drift. Expected baseline:

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

The command uses existing QA/QC service logic and verifies `QualityCheck`/`Metric` persistence through an in-memory test session. It does not require live providers, AI models, Docker or PostGIS.

`scripts/run_readiness_check.sh` runs this benchmark automatically, so any
change that alters the golden QA/QC metric baseline must update the fixture and
expected metrics deliberately.

### Demo workflow seed

Sprint 15 adds an explicit offline demo workflow seed. It creates or returns a
demo project, AOI, fixture reference buildings, fixture candidate buildings and
a persisted QA/QC result. It does not fetch live GRB/OSM data and does not run
AI inference.

API:

```bash
curl -X POST http://localhost:1202/api/v1/demo/workflow
```

CLI:

```bash
python scripts/seed_demo_workflow.py --json
```

In Docker Compose on a LAN host:

```bash
curl -X POST http://192.168.10.150:1202/api/v1/demo/workflow
```

### QA/QC result listing

Persisted project quality checks and metric rows can be listed with:

```bash
curl http://localhost:1202/api/v1/projects/{project_id}/quality-checks
```

The frontend QA/QC Results panel uses this endpoint after loading the demo
workflow or running QA.

### Export foundation

Persisted exports can be created from the existing workbench state:

```bash
curl -X POST http://localhost:1202/api/v1/exports/metadata \
  -H "Content-Type: application/json" \
  -d '{"project_id":"PROJECT_UUID"}'
```

Vector dataset GeoJSON export:

```bash
curl -X POST http://localhost:1202/api/v1/exports/geojson \
  -H "Content-Type: application/json" \
  -d '{"export_kind":"dataset","dataset_id":"DATASET_UUID"}'
```

Detection or segmentation run GeoJSON export:

```bash
curl -X POST http://localhost:1202/api/v1/exports/geojson \
  -H "Content-Type: application/json" \
  -d '{"export_kind":"detection_run","analysis_run_id":"ANALYSIS_RUN_UUID"}'
```

List and inspect exports:

```bash
curl http://localhost:1202/api/v1/exports/projects/PROJECT_UUID/exports
curl http://localhost:1202/api/v1/exports/EXPORT_UUID/content
```

Download an artifact as a browser/file response:

```bash
curl -OJ http://localhost:1202/api/v1/exports/EXPORT_UUID/download
```

Create a lightweight HTML project report artifact:

```bash
curl -X POST http://localhost:1202/api/v1/exports/report \
  -H "Content-Type: application/json" \
  -d '{"project_id":"PROJECT_UUID"}'
```

The report contains project, dataset, QA/QC summary and export history state
only. It is not a PDF designer and does not add a separate reporting module.

After rebuilding a Docker/LAN deployment, verify the end-to-end demo and export
flow through the browser-facing frontend proxy:

```bash
bash scripts/verify_demo_export_workflow.sh http://192.168.10.150:1202
```

The script seeds the explicit demo workflow, verifies persisted QA/QC results,
creates metadata/report/vector GeoJSON exports, lists exports and downloads the
JSON/GeoJSON/HTML artifacts.

### Backend import smoke

```bash
cd backend
python -c "from app.main import app; print(app.title)"
```

### Dockerized backend

```bash
docker compose up --build backend db
```

The Docker Compose stack does not require a root `.env` file for the default local runtime. The database service exposes a container-internal Postgres healthcheck, and the backend also runs `docker_start.sh`, which retries an actual SQL `SELECT 1` connection before running `python -m alembic upgrade head` and starting Uvicorn.

PostGIS is not published on the host `5432` port by default. This avoids conflicts with existing Postgres/PostGIS services on NAS or server hosts. The backend connects over Docker networking with `db:5432`.

Backend and frontend Docker build contexts exclude dependency folders, build outputs and Python bytecode caches via `.dockerignore`.

The Docker Compose frontend is published at `http://localhost:1202`.

Compose healthchecks are enabled for all runtime services:

- `db` uses `pg_isready`.
- `backend` checks `http://127.0.0.1:8000/health` inside the container.
- `frontend` checks `http://127.0.0.1/health` through nginx, which also verifies the frontend-to-backend proxy path.

The frontend waits for a healthy backend before starting. Check runtime state:

```bash
docker compose ps
docker compose logs --tail=80 backend
docker compose logs --tail=80 frontend
```

The backend Docker image installs the approved GIS runtime extra (`.[gis]`) so
browser-facing Docker deployments can report raster/vector processing
capabilities accurately:

- `rasterio`
- `numpy`
- `pillow`
- `geopandas`
- `pyogrio`
- GDAL/GEOS/PROJ system libraries

After rebuilding the backend image, verify the LAN/browser runtime from the
repository root:

```bash
bash scripts/verify_gis_runtime.sh http://localhost:1202
```

On a NAS or server host, use the published LAN URL:

```bash
bash scripts/verify_gis_runtime.sh http://192.168.10.150:1202
```

The script calls `/api/v1/system/capabilities` through the frontend proxy and
fails if `postgis`, `rasterio` or `geopandas` are not reported as available.

The backend Docker build also runs:

```bash
python scripts/gis_import_smoke.py
```

Inside the backend Docker build context this resolves to
`backend/scripts/gis_import_smoke.py`. The root `scripts/gis_import_smoke.py`
wrapper calls the same smoke locally. The smoke imports `rasterio`, `geopandas`
and `pyogrio`; if one of those imports fails, the backend image build fails
before deployment.

### Live Docker/PostGIS migration smoke

Sprint 11 validates the real PostGIS runtime path with the existing database service. From the repository root:

```bash
docker compose config
docker compose up -d db
DATABASE_URL=postgresql+psycopg://geointel:geointel@localhost:5432/geointel bash scripts/live_migration_smoke.sh
```

The smoke script:

- opens a backend SQLAlchemy connection and runs `SELECT 1`
- runs `alembic upgrade head`
- checks `PostGIS_Version()` after migrations have created the extension
- verifies one Alembic head
- verifies required migrated tables and GiST indexes exist

Expected local environment:

```bash
DATABASE_URL=postgresql+psycopg://geointel:geointel@localhost:5432/geointel
```

If the database is not reachable, confirm Docker Desktop is running and that port `5432` is not already occupied. To clean up the local database container without deleting the named volume:

```bash
docker compose stop db
```

To remove the local PostGIS volume as well, use only when you explicitly want a fresh database:

```bash
docker compose down -v
```

## Key docs
- `docs/API_CONTRACTS.md`
- `docs/DATABASE_IMPLEMENTATION_PLAN.md`
- `docs/DEFINITION_OF_DONE.md`
- `docs/40-build-launch/SPRINT_1_SCOPE_FREEZE.md`

## Raster dependency note

Raster metadata and raster operations depend on local GDAL/rasterio availability.

To enable raster processing locally:

```bash
python -m pip install rasterio
```

If `rasterio` is unavailable:
- raster metadata responses return `503` with `RASTER_PROCESSING_UNAVAILABLE`
- raster clip/tile endpoints return explicit unavailable responses

## Export report artifact

`POST /api/v1/exports/report` creates the existing lightweight
`project_report_html` artifact. The report is a self-contained HTML handoff
view rendered from persisted project, dataset, QA/QC and export-history state.
It includes readiness scorecards, dataset inventory, QA/QC evidence, artifact
history, known limitations and print-friendly CSS.

This remains a simple HTML export. It does not add a PDF designer, report
builder, live provider fetching or new analysis behavior.

## Vector area selection

`POST /api/v1/projects/{project_id}/datasets/{dataset_id}/vector/select` runs a
read-only EPSG:4326 bbox query against persisted PostGIS `vector_features` and
returns a canonical-envelope GeoJSON FeatureCollection. It is intended for the
Map workspace area-extract flow and does not create derived datasets or export
records by itself.

The same bounded endpoint is the canonical large-layer map delivery path. The
frontend requests at most 1,000 features for the current viewport and surfaces
the response `truncated` flag; the backend does not provide or imply an
unbounded municipality-wide map response.

`POST /api/v1/projects/{project_id}/datasets/{dataset_id}/vector/select/derive`
uses the same persisted `vector_features` selection but writes the result as a
new derived vector dataset. The created dataset uses
`source="operation:selection"`, `source_name="map_selection"` and
`derived_from_dataset_id` for source provenance, stores a GeoJSON artifact and
indexes its features back into `vector_features` for later QA/QC and analysis.

`POST /api/v1/exports/geojson` with `export_kind="vector_selection"` persists
the same bbox-selected FeatureCollection as a normal export record with
`export_type="vector_selection_geojson"`. This creates a handoff artifact only;
it does not create a derived dataset.

## Temporal Mol data and evolution

Dataset uploads accept `temporal_series_key`, `observed_at`, `valid_from`,
`valid_to`, `temporal_granularity` and `source_version`. Every new source or
derived dataset also writes dataset version 1 in the same transaction.

After the Mol municipality workspace is available, import the official source
snapshots explicitly:

```bash
docker exec geointel python /app/scripts/provision_mol_population_history.py
docker exec geointel python /app/scripts/provision_mol_historical_landuse.py
docker exec geointel python /app/scripts/provision_official_landuse_timeseries.py
```

The first command imports Statbel sector population for 2021-2025. The second
imports Digitaal Vlaanderen historical land use for 1778, 1873 and 1969. The
third imports the Departement Omgeving 10 m forest class for 2013, 2016, 2019,
2022 and 2025. All commands are idempotent, use the normal
API/DatasetService flow and retain fetched artifacts in persistent operator
storage. They never run on app startup.

Historical land-use work can be bounded explicitly:

```bash
docker exec geointel python /app/scripts/provision_mol_historical_landuse.py --years 1778 1969 --themes forest water
```

`GET /api/v1/projects/{project_id}/temporal/series` discovers the series and
`POST /api/v1/projects/{project_id}/temporal/compare` compares two snapshots
inside one EPSG:4326 bbox. Partial statistical sectors are estimates; old map
editions without stable identities do not produce invented object changes.
Modern raster-derived forest polygons have the same identity limitation. Their
area is measured in EPSG:31370 and is exact within the 10 m source
representation, not a cadastral forest survey.

## Helpful repository scripts

- `bash scripts/backend_install.sh`
- `bash scripts/backend_test.sh`
- `bash scripts/backend_dev.sh`
- `bash scripts/smoke_backend_import.sh`