Make dataset audit mandatory for training loop

backend/tests/test_belgium_training_loop.py

@@ -1,6 +1,9 @@
 from __future__ import annotations
 
 import importlib.util
+import json
+import subprocess
+import sys
 from pathlib import Path
 
 
@@ -29,3 +32,35 @@ def test_training_command_is_cuda_deterministic_and_bound_to_frozen_inputs(tmp_p
     assert "seed=42" in command
     assert "epochs=160" in command
     assert f"data={tmp_path / 'dataset.yaml'}" in command
+
+
+def test_loop_refuses_failed_dataset_audit(tmp_path: Path) -> None:
+    audit = tmp_path / "audit.json"
+    audit.write_text(json.dumps({"status": "needs_attention", "low_variance_positive_tile_count": 4}))
+    result = subprocess.run(
+        [
+            sys.executable,
+            str(SCRIPT),
+            "--initial-model",
+            str(tmp_path / "base.pt"),
+            "--train-yaml",
+            str(tmp_path / "dataset.yaml"),
+            "--dataset-audit",
+            str(audit),
+            "--calibration-summary",
+            str(tmp_path / "cal.json"),
+            "--test-summary",
+            str(tmp_path / "test.json"),
+            "--background-summary",
+            str(tmp_path / "background.json"),
+            "--corpus-manifest",
+            str(tmp_path / "manifest.json"),
+            "--output-dir",
+            str(tmp_path / "output"),
+        ],
+        capture_output=True,
+        text=True,
+        check=False,
+    )
+    assert result.returncode != 0
+    assert "Dataset audit is not ok" in result.stderr

docs/BELGIUM_BUILDING_TRAINING_LOOP.md

@@ -79,3 +79,5 @@ The active production model remains unchanged while any gate fails.
 
 Every failed assessment returns `continue_training_loop`. Only a report with
 `training_complete` may proceed to final human review and guarded activation.
+The orchestrator refuses to start unless the frozen dataset audit is `ok` and
+contains zero blank/low-variance positive tiles.

docs/CODEX_EXECUTION_LOG.md

@@ -11570,3 +11570,20 @@ Next gate:
   became Brussels `0.566`, Wallonia `0.427`, Flanders `0.222`. The generic-base
   iteration did not beat it. Both remained rejected, and iteration 4 started
   from the stronger candidate on the dated 75-AOI corpus.
+
+## 2026-07-27 - Positive-imagery gate and complete SPW campaign
+
+- Visual inspection caught 20 positive Walloon tiles with labels over white
+  no-data imagery. SPW 2024 is an official but partial campaign, so that
+  training run was stopped and its candidate is invalid.
+- Dataset QA now fails whenever any positive tile is blank/low-variance. The
+  checkpointed training orchestrator also refuses to start unless this audit
+  is `ok` with zero affected positive tiles.
+- Replaced the partial product with the official, territory-complete SPW summer
+  2023 campaign (27 May through 25 June, 25 cm). The frozen replacement corpus
+  `building-be-v5-dated-20260727-r1` retains 75 AOIs and 13,765 accepted labels.
+  Manifest SHA-256 is
+  `eade90d3ba22b426b72a300fcaa9a01f6eb1d18567c4a53413b72b8048f5f021`.
+- The v5 tile audit passed with zero blank positive tiles. The automated CUDA
+  loop started from the strongest prior candidate and will checkpoint every
+  train/calibrate/test/background assessment without promoting failed models.

docs/TODO.md

@@ -953,4 +953,5 @@ This file now starts with the current implementation status. Older preparation/b
 - [x] Freeze the objective train/evaluate/error-analysis loop and regional exit gates.
 - [x] Expand the second corpus wave to 60 independent AOIs and 10,262 accepted labels.
 - [ ] Resolve the v3 Flanders and Wallonia generalisation failures through additional training-only evidence and retraining.
-- [x] Replace rolling-mosaic training inputs with governed dated 2025 Flanders/Brussels and 2024 SPW imagery; retain exact flight-day limitations.
+- [x] Replace rolling-mosaic training inputs with governed dated 2025 Flanders/Brussels and complete 2023 SPW imagery; retain exact flight-day limitations.
+- [x] Reject positive labels over blank/no-data imagery and replace partial SPW 2024 coverage with the complete dated SPW 2023 campaign.

scripts/run_belgium_building_training_loop.py

@@ -78,6 +78,7 @@ def main() -> int:
     parser = argparse.ArgumentParser()
     parser.add_argument("--initial-model", type=Path, required=True)
     parser.add_argument("--train-yaml", type=Path, required=True)
+    parser.add_argument("--dataset-audit", type=Path, required=True)
     parser.add_argument("--calibration-summary", type=Path, required=True)
     parser.add_argument("--test-summary", type=Path, required=True)
     parser.add_argument("--background-summary", type=Path, required=True)
@@ -93,6 +94,11 @@ def main() -> int:
     args = parser.parse_args()
     if args.iterations < 1:
         raise SystemExit("--iterations must be positive")
+    dataset_audit = json.loads(args.dataset_audit.read_text(encoding="utf-8"))
+    if dataset_audit.get("status") != "ok":
+        raise SystemExit(f"Dataset audit is not ok: {args.dataset_audit}")
+    if int(dataset_audit.get("low_variance_positive_tile_count") or 0) != 0:
+        raise SystemExit("Dataset audit contains blank/low-variance positive tiles")
 
     state_path = args.output_dir / "training-loop-state.json"
     state: dict[str, Any] = {
@@ -101,6 +107,7 @@ def main() -> int:
         "started_at": datetime.now(UTC).isoformat(),
         "initial_model": str(args.initial_model),
         "train_yaml": str(args.train_yaml),
+        "dataset_audit": str(args.dataset_audit),
         "corpus_manifest": str(args.corpus_manifest),
         "iterations": [],
     }