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Add persisted false-positive visual review gate
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2026-07-13 17:12:59 +02:00
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## Next recommended pass
- Visually classify a stratified false-positive sample from Turnhout, Herentals and Geel before deciding whether any confirmed examples belong in a new hard-negative corpus. Review the remaining 5,838 persistent false negatives in the same evidence-led pass; do not start another blind training run.
# Sprint 176 - Detection false-positive visual review gate
## Persisted provenance
- Extended the existing read-only QA evidence GeoJSON conversion so detection-backed evidence carries the persisted detection id, job id, confidence, model name/version, source tile path and pixel bbox.
- Added equivalent persisted segmentation provenance fields without changing the endpoint, canonical envelope, ORM or migration chain.
- Historical `QualityCheck` evidence can be re-exported against existing persisted `Detection` rows; no QA rerun or data rewrite is required.
## Manual visual review
- Added a storage-root-confined contact-sheet renderer for persisted detection false-positive evidence.
- The renderer validates portfolio role counts, polygon geometry, source imagery and persisted provenance, then selects deterministically across AOI, WGS84 area bucket and confidence band.
- Source imagery is rendered with the candidate pixel bbox plus persisted matched-reference and missed-reference overlays.
- Added an explicit five-state review CSV: `confirmed_model_false_positive`, `reference_gap_or_change`, `qa_alignment_mismatch`, `uncertain` and `unreviewed`.
- Added a separate validator that rejects missing, duplicate, unexpected or invalid decisions. `--require-complete` exits with code `2` while any record remains unreviewed.
- Only explicitly confirmed model false-positives are emitted to `confirmed_model_false_positives.geojson`; no QA result is automatically converted into a model label or training artifact.
## Validation
- `python -m compileall backend/app`: passed.
- `python -m pytest`: 478 passed.
- Focused provenance/render/path-confinement/incomplete-review/export tests: passed.
- `python -m ruff check` for changed Python services, scripts and tests: passed.
- Generated fixture contact sheet was visually inspected at 128 px thumbnails; candidate/reference/missed-reference overlays and header provenance remained readable.
- `npm run typecheck`: passed.
- `npm run build`: passed; app bundle `217.00 kB`, MapLibre bundle `801.82 kB` before gzip.
- `bash scripts/run_readiness_check.sh`: passed with 478 tests and the new operator-script compile gates.
- `python -m alembic heads`: one head, `202606120900`.
- `python -m alembic upgrade head --sql`: complete migration chain rendered successfully.
- Local Docker validation remains unavailable because Docker CLI is not installed on the Windows host; live all-in-one/PostGIS validation follows on Tower after deployment.
## Next recommended pass
- Re-export the seven-AOI evidence portfolio from the deployed backend, render the Turnhout/Herentals/Geel sheets and inspect the real orthophoto evidence. Keep all CSV decisions `unreviewed` until an operator makes an explicit visual classification; do not start another model training run yet.