Detection QA reports a precision/recall curve, average precision and a
calibration sweep; segmentation QA reported a single operating point. Both rank
their outputs by confidence, so the same view applies, and the asymmetry meant
the two panels answered different questions about comparable runs — an
inconsistency introduced when detection gained the curve.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
tile_manifest_path arrives in the detection and segmentation request and was
read straight off disk, and a manifest entry may name an absolute tile path.
That makes an API field an unbounded reference to the host filesystem, and it
contradicts the rule the persistence model rests on: only a governed,
runtime-produced artifact may be consumed, and a file outside the storage root
is not one.
Both the manifest and every tile it names now resolve under STORAGE_ROOT.
Resolution happens before the comparison, so ".." cannot climb out and a
sibling that merely shares a name prefix does not pass.
GEOINTEL_ALLOW_EXTERNAL_ARTIFACT_PATHS opts out for provisioning workflows that
stage tiles before ingest.
The check honours the Settings the caller is operating under rather than the
process-wide ones, because every analysis path already threads its own.
The affected tests write manifests into tmp_path, so they now declare tmp_path
as the storage root — which is what a deployment does, and makes the fixtures
more honest than they were.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
/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>
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>
