Expand YOLO training AOIs safely

scripts/README.md

@@ -405,6 +405,43 @@ This refreshed cleanpx dataset is the minimum pre-training baseline after the
 visual contact-sheet pass found six blank-looking `arendonk_heide` validation
 negatives in the older export.
 
+The persisted false-negative audit subsequently showed that the cleanpx12
+candidate still misses about 79-92% of the comparable reference population and
+misses every reference building below 25 m2 in the seven-AOI review. Do not
+train another candidate from the same four positive training AOIs. Refresh the
+existing AOI1024 sample directory after pulling Sprint 171; existing files are
+reused and only the four new explicit positive AOIs need to be fetched:
+
+```bash
+docker exec -it geointel python3 /app/scripts/prepare_operator_real_data_samples.py \
+  --output-dir /app/storage/operator-data/operator-samples-1024 \
+  --width 1024 \
+  --height 1024 \
+  --half-size-scale 2
+```
+
+The expansion adds Olen, Lille, Oud-Turnhout and Kasterlee center as training
+samples. Turnhout, Retie, Westerlo and Arendonk-heide remain explicit validation
+holdouts in generated manifest provenance. The tile exporter defaults to those
+four holdouts and rejects a manifest-aware split that leaks one into training.
+Use the lower `min-label-px=4` profile first to measure small-building retention;
+it remains subject to dataset audit and visual contact-sheet review before any
+training:
+
+```bash
+docker exec -it geointel python3 /app/scripts/export_operator_yolo_tile_dataset.py \
+  --manifest-path /app/storage/operator-data/operator-samples-1024/operator_samples_manifest.json \
+  --output-dir /app/storage/operator-data/yolo-building-aoi1024-expanded-minpx4vis035 \
+  --tile-size 512 \
+  --stride 256 \
+  --negative-keep-ratio 1.0 \
+  --min-label-px 4 \
+  --min-label-visible-ratio 0.35 \
+  --drop-low-variance-negatives \
+  --blank-range-threshold 3 \
+  --force
+```
+
 Then audit with stricter small-box gates:
 
 ```bash