PolyFootNet: Extracting Polygonal Building Footprints in Off-Nadir Remote Sensing Images

Extracting polygonal building footprints from off-nadir imagery is crucial for diverse applications. Current deep-learning-based extraction approaches predominantly rely on semantic segmentation paradigms and postprocessing algorithms, limiting their boundary precision and applicability. However, existing polygonal extraction methodologies are inherently designed for near-nadir imagery and fail under the geometric complexities introduced by off-nadir viewing angles. To address these challenges, this article introduces the polygonal footprint network (PolyFootNet), a novel deep-learning framework that directly outputs polygonal building footprints without requiring external postprocessing steps. The PolyFootNet employs a high-quality mask prompter to generate precise roof masks, which guide polygonal vertex extraction in a unified model pipeline. A key contribution of the PolyFootNet is introducing the self-offset attention (SOFA) mechanism, grounded in Nadaraya–Watson regression, to effectively mitigate the accuracy discrepancy observed between low-rise and high-rise buildings. This approach allows low-rise building predictions to leverage angular corrections learned from high-rise building offsets, significantly enhancing overall extraction accuracy. Additionally, motivated by the inherent ambiguity of building footprint extraction (BFE) tasks, we systematically investigate alternative extraction paradigms and demonstrate that a combined approach of building masks and offsets achieves superior polygonal footprint results. Extensive experiments validate PolyFootNet’s effectiveness, illustrating its promising potential as a robust, generalizable, and precise polygonal BFE method from challenging off-nadir imagery. To facilitate further research, we will release pretrained weights of our offset prediction module at https://github.com/likaiucas/PolyFootNet