Expand operator samples for YOLO hard negatives

scripts/README.md

@@ -175,13 +175,18 @@ To prepare the documented operator samples reproducibly inside the all-in-one
 runtime container, run:
 
 ```bash
-docker exec -it geointel python /app/scripts/prepare_operator_real_data_samples.py --samples geel,mol,turnhout
+docker exec -it geointel python3 /app/scripts/prepare_operator_real_data_samples.py
 ```
 
 This writes GeoTIFF/GeoJSON pairs and `operator_samples_manifest.json` under
 `/app/storage/operator-data` inside the container, which maps to
-`storage/operator-data` in the Tower appdata checkout. The helper fetches only
-the explicit documented AOIs, records Digitaal Vlaanderen attribution and
+`storage/operator-data` in the Tower appdata checkout. The default corpus
+contains reference AOIs for Geel, Mol, Turnhout, Herentals, Balen, Retie and
+Westerlo plus background candidates for Postel-bos, Lommel-heide and
+Kasterlee-bos. Normal reference AOIs still fail when GRB returns no buildings;
+background candidates are explicitly marked with `sample_role` and may write an
+empty reference FeatureCollection for negative-tile training. The helper fetches
+only the explicit documented AOIs, records Digitaal Vlaanderen attribution and
 reuses existing files by default. Use `--force` only when the local runtime
 artifacts should be regenerated.
 
@@ -308,11 +313,11 @@ with overlapping raster windows:
 ```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 \
+  --output-dir /app/storage/operator-data/yolo-building-tile-expanded160 \
+  --tile-size 160 \
+  --stride 80 \
+  --negative-keep-ratio 1.0 \
+  --val-samples turnhout,retie,kasterlee_bos \
   --force
 ```
 
@@ -328,18 +333,38 @@ output directory:
 
 ```bash
 docker exec \
-  -e OPERATOR_YOLO_DATASET_DIR=/app/storage/operator-data/yolo-building-tile-dataset \
+  -e OPERATOR_YOLO_DATASET_DIR=/app/storage/operator-data/yolo-building-tile-expanded160 \
   -e YOLO_BASE_MODEL_PATH=/app/models/yolov8n.pt \
-  -e TRAIN_MODEL_OUTPUT_PATH=/app/models/geointel-building-tile-detector.pt \
-  -e TRAIN_EPOCHS=30 \
+  -e TRAIN_OUTPUT_DIR=/app/storage/training/operator-yolo \
+  -e TRAIN_RUN_NAME=geointel-building-yolov8n-expanded160e50 \
+  -e TRAIN_MODEL_OUTPUT_PATH=/app/models/geointel-building-yolov8n-expanded160e50.pt \
+  -e TRAIN_EPOCHS=50 \
   -e TRAIN_IMGSZ=256 \
-  -e TRAIN_BATCH=4 \
+  -e TRAIN_BATCH=8 \
   -e TRAIN_WORKERS=0 \
   -e TRAIN_DEVICE=cpu \
   -e PYTHON_BIN=python3 \
   geointel bash /app/scripts/train_operator_yolo_detector.sh
 ```
 
+Benchmark any trained candidate through the same persisted QA/QC matrix before
+using it operationally:
+
+```bash
+OPERATOR_SAMPLE_MANIFEST_PATH=storage/operator-data/operator_samples_manifest.json \
+OPERATOR_SAMPLE_SLUGS="geel mol turnhout retie 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" \
+MULTI_SAMPLE_OUTPUT_DIR=artifacts/detection-quality-matrix/multi-sample/expanded160e50-live \
+bash scripts/run_multi_sample_detection_quality_matrix.sh http://192.168.10.150:1202
+```
+
+The expanded 50-epoch candidate improved dense Geel/Mol/Turnhout/Retie scores,
+but the sparse Kasterlee-bos run still showed too many false positives. Treat it
+as the best current experimental dense-AOI candidate, not as a V1 default.
+
 Export calibration QA evidence for visual review:
 
 ```bash