Add multi-sample detection quality calibration

docs/AI_PIPELINES.md

@@ -167,6 +167,17 @@ download model weights. A zero detection count is valid as runtime evidence only
 when the selected model genuinely returns no usable detections after canonical
 class filtering; it does not prove the model is useful for the target imagery.
 
+Documented operator samples can be prepared inside the all-in-one runtime
+container:
+
+```bash
+docker exec -it geointel python /app/scripts/prepare_operator_real_data_samples.py --samples geel,mol,turnhout
+```
+
+The helper fetches explicit Digitaal Vlaanderen orthophoto/GRB GBG sample pairs
+for the documented AOIs only and writes `operator_samples_manifest.json`. The
+application itself still does not perform live provider fetching.
+
 For confidence-threshold calibration, use the sweep wrapper:
 
 ```bash
@@ -201,6 +212,23 @@ rankings `best_by_score`, `best_by_recall` and `best_by_precision` are operator
 decision aids only; GeoIntel still does not download models, seed fixture
 detections or treat AI detections as ground truth without QA/QC.
 
+To compare the same model/tile/threshold grid across all prepared operator
+samples, use:
+
+```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 multi-sample summary exposes `best_overall_by_score`,
+`best_overall_by_recall`, `best_overall_by_precision` and `best_by_sample` so
+model-quality decisions are based on repeated persisted QA/QC evidence rather
+than one AOI.
+
 For visual error inspection, export the persisted QA evidence from a calibration
 summary: