Harden YOLO tile inference inputs

docs/CODEX_EXECUTION_LOG.md

@@ -4285,14 +4285,19 @@ Changed:
 - Added `scripts/configure_yolo_model.py` to configure an existing local YOLO model into the deployment `.env` file without downloading model weights, loading a model or running inference.
 - The helper scans a mounted model directory for `.pt`, `.onnx` and `.engine` files, refuses no-model and ambiguous multi-model states, and writes env updates only when `--apply` is provided.
 - Added regression coverage in `backend/tests/test_sprint119_yolo_model_configuration.py` for no local model, ambiguous model selection, dry-run single model selection and env-file apply behavior.
-- Updated `scripts/README.md`, `deploy/unraid/README.md`, `backend/README.md`, `docs/TODO.md` and `CHANGELOG.md`.
+- Downloaded the official Ultralytics `yolov8n.pt` smoke model to Tower under `/mnt/user/appdata/geointel/models/yolov8n.pt`, recorded checksum `f59b3d833e2ff32e194b5bb8e08d211dc7c5bdf144b90d2c8412c47ccfc83b36`, and applied the env configuration with the local helper.
+- Hardened `YoloDetectionAdapter.predict_tile` so non-RGB raster tile artifacts are converted to a temporary RGB image before YOLO inference while georeferencing remains driven by the tile manifest.
+- Wrapped YOLO prediction runtime errors as typed `DETECTION_INFERENCE_FAILED` `AppError`s instead of leaking raw runtime exceptions through FastAPI.
+- Updated `scripts/README.md`, `deploy/unraid/README.md`, `backend/README.md`, `docs/AI_PIPELINES.md`, `docs/TODO.md` and `CHANGELOG.md`.
 
 Tested:
 - Red step: `python -m pytest backend\tests\test_sprint119_yolo_model_configuration.py -q` failed while `scripts/configure_yolo_model.py` was absent.
+- Red step: `python -m pytest backend\tests\test_sprint8b_yolo_foundation.py -q` failed because single-band TIFF tiles were passed through as mode `L` and prediction runtime errors leaked as raw `RuntimeError`.
 - `python -m pytest backend\tests\test_sprint119_yolo_model_configuration.py -q` (`4 passed`)
+- `python -m pytest backend\tests\test_sprint8b_yolo_foundation.py -q` (`12 passed`)
 - `python -m py_compile scripts\configure_yolo_model.py`
 - `python -m compileall backend/app`
-- `cd backend && python -m pytest -q` (`375 passed`, existing Pydantic protected-namespace warnings remain)
+- `cd backend && python -m pytest -q` (`377 passed`, existing Pydantic protected-namespace warnings remain)
 - `cd frontend && npm run typecheck`
 - `cd frontend && npm run build`
 - `bash scripts/run_readiness_check.sh` (`Run readiness check passed`)
@@ -4305,16 +4310,21 @@ Tested:
 - Deploy-time browser runtime verification passed for frontend, API proxy and icon.
 - Live API check passed: `GET /api/v1/detection/yolo/preflight` returned canonical `data` with `status=not_configured`, `YOLO_ENABLED=false`, `torch_version=2.12.1`, `ultralytics_version=8.4.89`, `will_download_models=false` and `will_run_inference=false`.
 - Tower helper dry-run passed: `python scripts/configure_yolo_model.py --models-dir /mnt/user/appdata/geointel/models --env-file .env --json` returned `status=no_model_found`, empty candidates and no env updates.
+- Tower model apply passed: `python scripts/configure_yolo_model.py --models-dir /mnt/user/appdata/geointel/models --env-file .env --model-file /mnt/user/appdata/geointel/models/yolov8n.pt --apply --json` returned `status=applied`, `YOLO_ENABLED=true` and `YOLO_MODEL_PATH=/app/models/yolov8n.pt`.
+- Live YOLO preflight with generated demo raster tile manifest passed with `status=ready`, `model_load_ok=true`, `manifest_valid=true`, `tile_paths_exist=true`, `tile_count=1`, `will_download_models=false` and `will_run_inference=false`.
+- Live inference smoke before the RGB adapter fix reproduced the runtime bug: YOLOv8n expected 3 channels but the demo tile was single-band (`input[1, 1, 480, 640]`).
 
 Open:
-- No local YOLO model file is currently present on Tower under `/mnt/user/appdata/geointel/models`, so runtime YOLO activation remains intentionally not configured until the operator places a real model file.
+- A local YOLO smoke model is now present and configured on Tower, but it is the generic COCO `yolov8n.pt` model. It proves the runtime path, not production-quality aerial building detection.
 
 Limitations:
-- Operational configuration helper only; no AI inference behavior, model download behavior, backend API contract, database migration, provider fetching or frontend product workflow changed.
+- The bundled Tower model file was placed as an operator/runtime artifact under appdata, not committed to Git.
+- The configured model is a generic COCO model and should be replaced by a suitable aerial/building detector for meaningful GIS output.
+- No model download behavior was added to the application; the manual operator placement remains explicit.
 - If multiple local model files are present, the operator must choose one with `--model-file` so GeoIntel does not silently activate the wrong model.
 
 Next recommended pass:
-- Place a real local model under `/mnt/user/appdata/geointel/models`, run the configurator with `--apply`, redeploy/restart the all-in-one container, then run the YOLO preflight with `--check-model-load` before any detection test run.
+- Deploy the RGB adapter fix to Tower, rerun the real YOLO inference smoke against the generated demo raster tile manifest, then replace `yolov8n.pt` with a domain-appropriate aerial/building detector before evaluating QA/QC quality.
 
 ## Sprint 104 AI Lab action guardrails (2026-06-24)