7 Commits

Include renames

Author	SHA1	Message	Date
JensandClaude Opus 5	2cd2c49389	calibrate a confidence threshold from one inference pass ... Threshold calibration ran the model over every tile once per threshold — three GPU passes to compare 0.50, 0.25 and 0.15 on a hundred-tile raster. The answer is already in a single run at the lowest value: detections above a higher cut are a subset of it, and duplicate suppression walks candidates in descending confidence, so a lower-confidence box can never displace a higher-confidence one. The kept set above any cut is identical whichever threshold the run used, which is what makes one pass sufficient rather than merely cheaper. QA now takes calibration_thresholds and reads each operating point off the same precision/recall walk it already performs, marking the F1-optimal cut. The lab runs inference once and fills its table from the sweep. The contract test asserted the per-threshold loop by name, pinning the waste it was meant to describe. It now states what calibration owes an operator: a row per requested threshold, from one run. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-08-22 19:37:19 +02:00
JensandClaude Opus 5	5b3839dc89	page detection and segmentation results instead of returning all of them ... /detection/runs/{id}/detections and its GeoJSON sibling returned every persisted detection, as did the segmentation equivalents. A regional run holds tens of thousands, and these are the endpoints the results table and the map overlay call after every run. They now take limit and offset, default to 2.000, and report total, limit, offset and truncated so the complete population stays visible while what is transferred does not. The GeoJSON responses carry the same window in a geointel_result_window foreign member. Rows are ordered by confidence, so a capped overlay draws the strongest detections rather than an arbitrary slice, and the lab says how many of how many are being shown rather than silently presenting a page as the whole run. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-08-22 15:24:46 +02:00
JensandClaude Opus 5	08188005bd	correct the tiled inference chain and move runs off the request thread ... Tile handling produced results that were wrong before any model quality question arose: - orthophoto tiles reached the model through PIL convert("RGB"), which truncates the high byte of a 16-bit product and treats a 4-band RGB+NIR tile's infrared channel as colour. Tiles are now read with rasterio, the visible bands are chosen explicitly, and values are percentile-stretched across all three bands together so hue is preserved; - an object wider than the tile overlap was truncated by both tiles into two boxes that barely intersect, so IoU suppression kept both: two false positives and one missed footprint per seam building. Suppression now also compares overlap against the smaller box, and boxes cut by an interior tile edge are dropped in favour of the neighbouring tile's complete view; - georeferencing fell back to an assumed EPSG:4326 when a manifest carried no CRS, producing geometry that renders plausibly in the wrong place. QA already refused such a tile; inference now fails closed too. Segmentation QA scored candidates against every reference feature in the dataset, so every building outside the inferred tiles counted as a false negative. It now applies the same persisted tile coverage that detection QA has always used, including the indexed ST_Intersects prefilter. Duplicate suppression uses an STRtree instead of the O(n^2) scan, tiles are predicted in batches of YOLO_BATCH_SIZE (a setting that existed but was never read), and detection/segmentation runs can be queued through /run-async for a polling background worker rather than holding an HTTP worker thread for minutes of GPU work. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-08-22 14:32:44 +02:00
Codex	0fae53a7de	Harden RC7 API response contracts	2026-07-18 05:07:35 +02:00
Codex	6e2a8cbdc4	Add local model asset catalog	2026-07-06 20:59:03 +02:00
Codex	7aa9382c9e	Surface YOLO preflight in Detection Lab	2026-07-06 10:52:26 +02:00
Codex	6ea3586a3e	Initial GeoIntel V1 foundation	2026-06-16 23:36:32 +02:00