Fix runtime GIS uploads for operator QA

scripts/README.md

@@ -318,7 +318,6 @@ docker exec \
   -e TRAIN_BATCH=2 \
   -e TRAIN_WORKERS=0 \
   -e TRAIN_DEVICE=cpu \
-  -e PYTHON_BIN=python3 \
   geointel bash /app/scripts/train_operator_yolo_detector.sh
 ```
 
@@ -328,6 +327,9 @@ Ultralytics from the existing runtime, copies the best trained artifact to
 the resulting `.pt` file like any other local model asset: verify preflight,
 run the real-data matrix and compare persisted QA/QC metrics before activating
 it as a useful default.
+Inside the all-in-one image the wrapper prefers
+`/opt/geointel/venv/bin/python` when that AI runtime exists. Set `PYTHON_BIN`
+only when intentionally overriding the interpreter.
 
 When whole-image training does not improve QA/QC, export a tile-level dataset
 with overlapping raster windows:
@@ -383,6 +385,12 @@ Current Tower audit status:
 - `yolo-building-tile-uniquehardneg160`: clean expanded-background baseline;
   576 tiles, 346 positive, 230 negative, 11,757 labels, 0 invalid labels and
   0 repeated background negatives in the first Tower audit.
+- `yolo-building-aoi1024-visible025`: larger AOI candidate baseline; 144
+  tiles, 117 positive tiles, 27 negative tiles, 15,079 labels and
+  `min_label_visible_ratio=0.25`. Its audit remains `needs_attention` because
+  the median normalized box area is still small. The trained
+  `geointel-building-yolov8s-aoi1024visible025e50-pt` asset is inactive until
+  persisted QA/QC and hard-negative promotion gates pass.
 
 After rebuilding the all-in-one image, the operator scripts are available inside
 the container at `/app/scripts/...`. Before rebuilding, use the host checkout or
@@ -424,7 +432,6 @@ docker exec \
   -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
 ```