Add hard-negative balanced YOLO tile export

scripts/README.md

@@ -328,6 +328,27 @@ negative tiles, and records `yolo_tile_dataset_summary.json` with
 It remains operator tooling only: no provider fetch, no API mutation and no
 automatic model training.
 
+For hard-negative-balanced experiments, repeat only train-split negative tiles
+from samples marked `sample_role=background_candidate`:
+
+```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-hardneg160r8 \
+  --tile-size 160 \
+  --stride 80 \
+  --negative-keep-ratio 1.0 \
+  --background-negative-repeat 8 \
+  --val-samples turnhout,retie,kasterlee_bos \
+  --force
+```
+
+The repeat option can also be set with
+`OPERATOR_YOLO_BACKGROUND_NEGATIVE_REPEAT`. It does not duplicate validation
+tiles, positive tiles or normal reference-sample negatives. Repeated background
+tiles receive deterministic `_hnXX` filenames and tile metadata records
+`sample_role`, `repeat_index` and `is_repeated_background_negative`.
+
 Train against the tile dataset by pointing the existing wrapper at the tile
 output directory:
 
@@ -388,6 +409,13 @@ was clean on Postel-bos and Lommel-heide at thresholds `0.25` and `0.15`, but
 produced 38 detections on Kasterlee-bos even at `0.25`. That blocks it from
 becoming a V1 default until a hard-negative-balanced candidate improves.
 
+The hard-negative-balanced `geointel-building-yolov8n-hardneg160r8e40-pt`
+candidate reduced Kasterlee-bos false-positive pressure to 5/9/25 detections
+at thresholds `0.25`/`0.15`/`0.05` and stayed at 0 detections on Postel-bos and
+Lommel-heide across all tested thresholds. It also regressed dense-AOI F1
+against `geointel-building-yolov8n-expanded160e50-pt`, so it is useful model
+quality evidence but not a V1 default.
+
 Export calibration QA evidence for visual review:
 
 ```bash