{"name": "beaconGIS \u2014 Building Damage Assessment", "package_name": "beacongis", "description": "AI building-level damage classification from pre/post-disaster RGB imagery, using a Siamese U-Net ensemble running on ONNX Runtime.", "about": "Classifies buildings into No Damage, Minor, Major, or Destroyed using a two-network deep-learning pipeline (LocalizationUNet + SiameseUNet, SeResNeXt-50 (32x4d) encoder, two-model ensemble) trained on the full public xView2/xBD dataset with Inria Aerial Image Labeling pretraining. xView2 official scorer Combined F1 = 0.7312. Designed for disaster-response teams: includes AOI clipping, CPU Fast Mode for non-GPU machines, automatic CRS-aware pre/post alignment, GSD normalization, offline template-driven SitRep generator (no LLM API), GeoPackage / GeoTIFF mask / JSON sidecar export. Runs entirely on ONNX Runtime (no PyTorch install required). Model weights (~234 MB fp16, CC BY-NC-SA 4.0) are downloaded on first use from the plugin's GitHub release page over plain HTTPS with SHA-256 verification \u2014 no account or token required. Outputs are draft assessments for human review \u2014 not authoritative ground truth.", "homepage": "https://beacon-gis.com/", "repository": "https://github.com/azeldev/beaconGIS", "tracker": "https://github.com/azeldev/beaconGIS/issues", "author": "Adem \u00d6zel", "tags": ["vector", "raster", "remote sensing", "change detection", "deep learning", "satellite imagery", "onnx", "humanitarian", "building damage", "disaster response", "xview2", "siamese unet", "xbd"], "downloads": 166, "latest_version": "1.1.0", "versions": [{"version": "1.1.0", "experimental": false, "qgis_min": "3.16.0", "qgis_max": "4.99.0", "downloads": 94, "uploaded_by": "azeldev", "upload_datetime": "2026-06-11T12:22:38.193511"}, {"version": "1.0.0", "experimental": false, "qgis_min": "3.16.0", "qgis_max": "4.99.0", "downloads": 72, "uploaded_by": "azeldev", "upload_datetime": "2026-06-05T01:48:40.703103"}]}