Abstract :

Building footprint extraction from high-resolution satellite imagery requires accurate building boundary raster masks and an effective shape reconstruction method to produce natural building footprints. Unlike vertex-centric graph approaches or mask contour tracing, we propose DINO-EdgeQuery, an edge-first paradigm that addresses this need. A frozen DINOv3 backbone, adapter neck, and an instance decoder inspired by Mask2Former provide building-aware boxes and masks, while a region of interest (ROI)-conditioned EdgeQuery decoder predicts edge activity, center, direction, length, and successor relations. By capturing dominant wall structures as edge primitives and reconstructing vertices purely through deterministic line intersections, we deliberately separate neural edge prediction from verifiable geometric assembly. Active edges are ordered by successor scores, and the P2 quality gate removes invalid or contained duplicate polygons. This geometric post-processing encourages sharp, line-intersection-based corners and enforces topology-safe polygons through deterministic validity checks. To improve generalization, we train segmentation and EdgeQuery branches separately, mask inactive losses to zero, and merge the trained weights for unified inference. Validation on the dataset publicly released on the website by the Wuhan University Geospatial Computer Vision Group demonstrates that our EdgeQuery framework, including the P2 quality gate, delivered high-quality, geometrically superior polygon outputs with zero self-intersections and triangle collapses, and simultaneously improved Intersection over Union (IoU) and suppressed duplicate building footprints.

Orbitalnet discusses its journey toward realizing geospatial-Intelligence and what lies ahead.