/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>
evidence_geojson emitted one feature per false positive, one per false negative
and two per match, with no limit. A regional check of 40k detections against 45k
reference footprints produced well over a hundred thousand features in a single
response, plus one warning string per unresolvable identifier. The endpoint the
entire review workflow depends on therefore failed exactly where review matters
most.
What to draw is now decided before any geometry is fetched, so the query work is
proportional to the result rather than to the size of the check — previously
130k geometries were resolved through an IN clause holding every identifier in
the check, to then discard most of them.
The budget is split between misses and false positives in proportion to their
populations with at least one of each, rather than by strict priority, which
would mean a check with 50.000 misses and three false positives never showed
one. Confirmations fill what remains, and a match is kept or dropped as a pair
because half a match is not reviewable evidence.
limit_evidence and evidence_role_counts are removed: plan_evidence supersedes
them, and helpers kept alive only by their own tests read like a contract.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
The temporal timeline summarises every snapshot in a series. Each summary now
also counts how many features the selection edge cuts, but a timeline point
renders values only, so that was one database round trip per snapshot for a
disclosure nobody sees. Make it opt-out and have the timeline opt out.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
The vector selection export caps its features and recorded truncated on the
export record. The file said nothing: an operator downloads
mol-selection.geojson, opens 250 buildings in QGIS where the workbench said
1.400, and has nothing to tell them the difference. For a product whose promise
is that an export is a reproducible result, a file that looks complete and is
not is the sharpest possible violation of it.
Every exported FeatureCollection now carries a geointel_provenance foreign
member — RFC 7946 requires parsers to ignore unknown members, so QGIS and
ogr2ogr are unaffected, and the detection export already used the same
convention for its trust classification. It names the source, the dataset and
its edition, the selection, and whether the file is complete; completeness is
derived from the counts as well as the flag, so a caller that forgets the flag
cannot produce a file claiming to hold everything.
Applied uniformly to the selection, dataset, detection and segmentation
exports, which previously disclosed three different amounts of nothing.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Change detection was the one analysis that ignored the selection entirely. It
compared two datasets in full, loaded every feature of both into Python with no
spatial predicate, and — with include_unchanged defaulting to true — returned a
FeatureCollection holding both datasets. For a regional building layer that is
the wrong answer to "what changed here" and a response no browser should be
asked to hold.
It now accepts bbox and area_id, resolved the way every other analysis resolves
them, and loads through an indexed ST_Intersects predicate.
Features are deliberately not clipped to the selection. A change class
describes a whole object: comparing a clipped earlier footprint against an
unclipped later one would report the selection edge itself as a change. Objects
the edge crosses are compared in full and counted in a warning.
The returned geometry is capped by preview_limit, spending that budget on
modified, added and removed before unchanged, while every count still describes
the whole selection.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
disclosure from data
Change detection had only added/removed/unchanged, so a building extended by
an annexe dropped below the IoU threshold and was reported twice: once as
removed and once as added. That hides exactly the category a change-detection
product exists to show and inflates both counts. A "modified" class now covers
the band between the modified floor and the unchanged threshold.
Matching also ran as a full cross product with no spatial index, unlike the QA
matcher beside it: two municipal building layers meant hundreds of millions of
geometry intersections. It uses an STRtree and considers larger footprints
first, so a big footprint is not left over after a small neighbour claimed its
counterpart.
The assistant guaranteed honesty about estimated values by rewriting the
model's sentences with regular expressions, which only fires when it
recognises the phrasing the model happened to produce. estimate_disclosures
derives the same statement from the metric metadata, so it holds regardless of
how the answer was worded. The prose substitution stays as a second layer.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Four ways a selection produced a confident number about a different area than
the operator drew:
Flood hazard divided the inundated cells by every cell in the drawn rectangle,
including cells the VMM raster does not model at all. A selection reaching
past the modelled extent therefore reported a diluted risk share, turning
missing data into an implied absence of risk. Terrain, bathymetry and thematic
raster already divided by valid cells; flood hazard was the outlier. It now
reports the three populations separately, states model coverage next to the
drawn area, and returns a null fraction rather than a zero when nothing was
modelled.
geometry_mask selects a cell when its centre falls inside the geometry, so a
rectangle smaller than one cell — or one landing between four centres —
selected nothing and the analysis returned zeros indistinguishable on screen
from "we looked and there is nothing here". On a 100 m population raster a
40 m rectangle over a city block reported no inhabitants. Selection now falls
back to the touched cells and says that it did, since the answer then covers
more ground than was requested. rasterio.mask applies the same centre rule
when cropping, so that call is widened too; the cells that count are still
decided by the centre rule wherever it selects anything.
The object count treated any feature touching the selection as whole, while
intersection_area clipped it — two headline numbers on one panel describing
different populations. The count stays whole-feature, which is what "objecten"
means to an operator, but now reports how many the edge cuts and is marked an
estimate when it does. The area_weighted_sum branch reuses that same count
instead of issuing its own near-identical query.
Partitioned selection de-duplicated the count on source_feature_id but
returned the raw rows, so a building on a municipal boundary was counted once
and drawn twice.
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>
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>
