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feat: SAR-based flood detection - #86
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…assification - flood_classification: separate flood from permanent water via reference (JRC GSW occurrence) or pre/post change detection; refuses to guess when neither is available - ensemble: detect() now returns a 3-class classified_mask when a reference or pre-event scene is supplied - flooding_sar: FloodingSARAnalysis (Sentinel-1 VV/VH) behind the standard analysis contract, registered and discoverable - gee_downloader: Sentinel-1 GRD + JRC Global Surface Water fetch (synthetic fallback, explicitly tagged) - inference/flood_pipeline: bbox -> S1 + JRC -> ensemble -> 3-class result - api: /api/predict dispatches flooding_sar to the SAR pipeline; type listed in analysis-types/health; config + [flood] extra added - scripts: eval_flood.py (real IoU/F1/precision/recall on Sen1Floods11) and download_sen1floods11.py - tests: classification + SAR analysis + registry/API exposure
NGO/gov actionability: surface affected_road_km (OSM, degrades honestly when osmnx absent) and a quality block stating is_synthetic, whether permanent vs flood was distinguished, the reference source, and that the detector is not yet validated against a labeled benchmark. No fabricated impact or accuracy.
require_api_key now validates keys against the organizations table (active keys only) with a 5-min cache, replacing the stub that accepted nothing but cv_dev. The cv_dev bypass is disabled unless CLIMATEVISION_ALLOW_DEV_KEY is set, so production rejects it by default. Adds auth tests (real key, unknown key, deactivated org, dev-key gate, e2e predict).
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feat: SAR-based flood detection
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Flood detection U-Net, SAR preprocessing, ensemble, OSM road-impact module, and tests.
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