Record Tower YOLO runtime validation

CHANGELOG.md

@@ -1417,3 +1417,4 @@ Added:
 - Expanded the explicit operator background-candidate AOI registry from 3 to 9 unique hard-negative locations and added tests for diversity/spread before further YOLO training.
 - Fixed the all-in-one Dockerfile so documented operator scripts are copied into `/app/scripts/`, then prepared and audited the new Tower `yolo-building-tile-uniquehardneg160` dataset as the next training candidate.
 - Fixed the YOLO preflight CLI so it respects environment-provided runtime configuration instead of reporting `not_configured` unless CLI flags were supplied.
+- Rebuilt the Tower all-in-one image with AI dependencies and verified live migration smoke, browser runtime and YOLO preflight readiness against an existing raster tile manifest.

docs/CODEX_EXECUTION_LOG.md

@@ -5698,12 +5698,17 @@ Open:
   - 16 samples, 13 positive samples, 9 background samples
   - 11,757 labels, 0 missing label files, 0 invalid label rows
   - repeated background negative share 0.0
+- Tower all-in-one rebuild from commit `f949347` with `GEOINTEL_INSTALL_AI=true`:
+  - live migration smoke passed.
+  - browser runtime verification passed on `http://192.168.10.150:1202`.
+  - `/app/scripts/prepare_operator_real_data_samples.py`, `/app/scripts/export_operator_yolo_tile_dataset.py` and `/app/scripts/audit_operator_yolo_dataset_quality.py` are present in the rebuilt container.
+  - YOLO preflight with an existing raster tile manifest returned `status: ready`, `dependencies_available: true`, `model_file_exists: true`, `tile_paths_exist: true`, `will_download_models: false` and `will_run_inference: false`.
 
 ## Known limitations
 
 - Some background candidates contain real GRB buildings. They are still useful as mixed rural/background samples, but the pure negative pressure currently comes mostly from Postel-bos, Lommel-heide and Arendonk-heide plus empty tiles inside sparse candidates.
-- The running Tower container was updated via temporary `docker cp` for live data prep before the Dockerfile copy fix existed; a normal rebuild is needed for `/app/scripts/prepare_operator_real_data_samples.py` to exist inside the image automatically.
+- The rebuilt Tower image now contains the operator scripts automatically. The generated `yolo-building-tile-uniquehardneg160` dataset is ready for a controlled training candidate, but no model has been promoted from it yet.
 
 ## Next recommended pass
 
-- Rebuild the Tower all-in-one image, then use `yolo-building-tile-uniquehardneg160` as the next safer hard-negative training dataset candidate. Benchmark after training before changing defaults.
+- Use `yolo-building-tile-uniquehardneg160` as the next safer hard-negative training dataset candidate. Benchmark after training before changing defaults.

docs/TODO.md

@@ -452,6 +452,6 @@ This file now starts with the current implementation status. Older preparation/b
 - [x] Keep every new background AOI explicit, `allow_empty_reference=True`, and `sample_role='background_candidate'`.
 - [x] Add test coverage for minimum background candidate count, unique centers and regional spread.
 - [x] Prepare the new samples on Tower and build a fresh hard-negative tile dataset.
-- [ ] Rebuild Tower all-in-one image so the newly copied operator scripts are available inside `/app/scripts` without `docker cp`.
+- [x] Rebuild Tower all-in-one image so the newly copied operator scripts are available inside `/app/scripts` without `docker cp`.
 - [x] Fix YOLO preflight CLI so it respects Tower `.env` runtime configuration.
 - [ ] Train a new candidate from `yolo-building-tile-uniquehardneg160` and run the positive/background promotion gates before activating it.