feat: add governed nationwide AOI orchestration and CUDA enforcement

docs/AI_PIPELINES.md

@@ -92,7 +92,7 @@ Ultralytics/PyTorch compatibility, but it still does not run tile prediction and
 does not download weights. It cannot be combined with `--assume-dependencies`
 because that would turn the smoke into a false positive.
 
-Docker and Unraid runtime support remains opt-in. Set `GEOINTEL_INSTALL_AI=true`
+Docker AI dependencies remain opt-in. Set `GEOINTEL_INSTALL_AI=true`
 at build time to install the backend `.[gis,ai]` extra into the container. Leave
 it unset or `false` for the default GIS-only image. Runtime model files should be
 mounted into the container, for example `/app/models/local-model.pt`, and enabled
@@ -109,6 +109,11 @@ Environment variables:
 - `YOLO_MODEL_DISPLAY_NAME`
 - `YOLO_MODEL_VERSION`
 - `YOLO_DEVICE`
+- `YOLO_REQUIRE_CUDA` (set to `true` on the production server; inference then
+  fails closed when CUDA is unavailable or `YOLO_DEVICE` selects CPU)
+- `YOLO_MODEL_CLASSES` (the active promoted detector is `building` only)
+- `YOLO_ENFORCE_VALIDATION_SCOPE` and `YOLO_VALIDATED_AREA_NAMES` (production
+  rejects inference when the raster is not bound to a persisted validated Area)
 - `YOLO_IMAGE_SIZE`
 - `YOLO_MAX_TILES`
 - `YOLO_MAX_DETECTIONS`