SPNet: segmentation-to-poly network for building footprint extraction

Building extraction from remote sensing images is a core task in urban planning and related applications. However, achieving high-precision building footprint extraction remains challenging due to complex building shapes and background noise. Existing mask-based methods produce raster masks with jagged edges, which generate numerous redundant corners after vectorization. In contrast, vector-based methods rely on complex multi-stage prediction pipelines, leading to large cumulative errors that degrade the final extraction performance. To address these issues, this paper proposes a building footprint extraction method named SPNet, which generates compact building polygons using only a single prediction pipeline based on semantic segmentation. First, corner regions within a defined area are annotated on the ground truth masks to convert sparse corner supervision into dense corner-region supervision for network training. Then, a backbone network with good performance and efficiency is adopted for feature extraction, and the LASS module, which combines SS2D-based global modeling and Natten-based local attention, is introduced in the feature fusion stage to improve the extraction of both overall building structures and local corner details. Finally, building polygons are generated by computing corner coordinates and sorting corners using the predicted corner and building masks. SPNet adopts a concise and efficient structure with a streamlined pipeline. It achieves competitive results on the WHU and CrowdAI datasets.