Automated Geometric Correction of OpenStreetMap Buildings via Context- and Boundary-Aware Segmentation
This study proposes a two-stage framework that integrates SegFormer, which excels in global context recognition, with SAM 2, which is capable of precise boundary segmentation, and offers scalability applicable to any region worldwide without additional label construction, provided that high-resolution true orthoimagery and OSM data are available.