Add operator YOLO training dataset tooling

docs/AI_PIPELINES.md

@@ -229,6 +229,42 @@ The multi-sample summary exposes `best_overall_by_score`,
 model-quality decisions are based on repeated persisted QA/QC evidence rather
 than one AOI.
 
+When repeated public model benchmarks remain too weak, the operator can convert
+the prepared real-data samples into a local YOLO training dataset:
+
+```bash
+docker exec -it geointel python /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
+```
+
+The exporter creates a standard YOLO detection layout with `dataset.yaml`,
+`images/train`, `labels/train`, `images/val` and `labels/val`. It converts GRB
+building reference geometries to pixel-space bounding boxes for the matching
+orthophoto sample and records `yolo_dataset_summary.json`.
+
+A minimal local training smoke can then be run explicitly in an AI-enabled
+runtime:
+
+```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 \
+  geointel bash /app/scripts/train_operator_yolo_detector.sh
+```
+
+This remains operator tooling only. GeoIntel does not expose Training Studio in
+V1, does not generate labels from predictions and does not treat the trained
+artifact as useful until it passes the same real-data Detection + QA matrix.
+
 For visual error inspection, export the persisted QA evidence from a calibration
 summary: