5 Commits

Include renames

Author	SHA1	Message	Date
JensandClaude Opus 5	2b968b74cf	make QA scoring reproducible and threshold-independent ... The greedy IoU matcher gave a reference to whichever candidate was offered first. Row order decided that, and every detection in a run shares one transaction timestamp, so ordering by created_at left the assignment undefined: the same QA run over the same data produced different mean IoU, and the geometry shown to a reviewer as a false positive could be the better of two detections. Candidates are now ranked by confidence with feature identity as tiebreaker, which is also the COCO/PASCAL rule. A single precision/recall/F1 triple describes one operating point, so two models cannot be compared from it: a conservatively calibrated model looks worse at a low confidence cut and better at a high one without detecting anything differently. DetectionMetricsService adds the full curve, average precision and the threshold where F1 actually peaks. Also: - report the population the metrics were computed over, so matches + false_positives equals candidate_feature_count even under an area filter; raw dataset totals move to the _raw fields; - state whether candidates are axis-aligned boxes or footprint polygons. A box can never reach IoU 1 against a rotated building, so the strict score has a ceiling that has nothing to do with detection quality. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-08-22 14:31:21 +02:00
Jens	5b3c17b494	feat(provenance): govern source snapshots and data inputs	2026-08-01 23:46:17 +02:00
Codex	0cad8fdf76	fix: bound detection QA to inference coverage	2026-07-15 00:35:39 +02:00
Codex	368c1ad738	Persist QA feature evidence	2026-06-25 02:57:50 +02:00
Codex	6ea3586a3e	Initial GeoIntel V1 foundation	2026-06-16 23:36:32 +02:00