Classify operator background corpus

docs/TODO.md

@@ -121,7 +121,8 @@ This file now starts with the current implementation status. Older preparation/b
 - [x] Train and gate `geointel-building-yolov8s-aoi1024clean512e50-pt` through seven positive AOIs and nine hard-negative/background AOIs.
 - [x] Train and gate background-aware `geointel-building-yolov8s-aoi1024bg512r3e50-pt`; it is the strongest positive-AOI candidate so far but remains inactive because full background-candidate false-positive pressure still blocks default promotion.
 - [x] Add explicit operator detection profiles for local model assets: balanced review around threshold `0.15` and conservative high-precision review around threshold `0.35`, both clearly marked as non-default-approved until promotion gates pass.
-- [ ] Split the background corpus into pure-empty negatives and sparse-building contextual AOIs, then retrain or recalibrate against the cleaner gate.
+- [x] Add pure-empty versus sparse-building contextual background corpus classification to operator manifests, hard-negative matrix filters and YOLO tile provenance.
+- [ ] Retrain or recalibrate against the cleaner pure-empty gate plus separate sparse-context inspection matrix.
 - [ ] Promote a V1 default building detector only after it passes seven positive AOIs, clean hard-negative/background gates and persisted QA/QC evidence without fake detections or model downloads.
 
 ## Sprint 8 status