3 Commits

Include renames

Author	SHA1	Message	Date
JensandClaude Opus 5	12aaf1bb4d	bound change detection to the operator's selection ... 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>	2026-08-22 14:56:15 +02:00
JensandClaude Opus 5	23d6e0372b	distinguish a redrawn footprint from a demolition, and derive estimate ... 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>	2026-08-22 14:33:37 +02:00
Codex	01f063b921	Add vector change detection foundation	2026-06-16 23:55:52 +02:00