PolyFootNet：从离天底遥感图像中提取多边形建筑足迹
PolyFootNet: Extracting Polygonal Building Footprints in Off-Nadir Remote Sensing Images
AI总结：
提出PolyFootNet，一种直接输出多边形建筑足迹的深度学习框架，通过自偏移注意力机制解决离天底图像中低层与高层建筑精度差异，结合掩膜与偏移实现高精度提取。
摘要：
从离天底（off-nadir）图像中提取多边形建筑足迹对于多种应用至关重要。当前的基于深度学习的提取方法主要依赖语义分割范式和后处理算法，限制了其边界精度和适用性。然而，现有的多边形提取方法本质上是为近天底（near-nadir）图像设计的，在离天底视角引入的几何复杂性下失效。为解决这些挑战，本文引入了多边形足迹网络（Polygonal Footprint Network，PolyFootNet），一种新颖的深度学习框架，可直接输出多边形建筑足迹，无需外部后处理步骤。PolyFootNet采用高质量掩膜提示器（High-Quality Mask Prompter）生成精确的屋顶掩膜，在统一模型流程中引导多边形顶点提取。PolyFootNet的一个关键贡献是引入基于Nadaraya-Watson回归的自偏移注意力机制（Self Offset Attention），有效缓解低层与高层建筑之间观察到的精度差异。该方法使低层建筑预测能够利用从高层建筑偏移中学习到的角度校正，显著提升整体提取精度。此外，鉴于建筑足迹提取任务固有的模糊性，我们系统性地探索了替代提取范式，并证明建筑掩膜与偏移相结合的方法可获得更优的多边形足迹结果。大量实验验证了PolyFootNet的有效性，展示了其作为从具有挑战性的离天底图像中提取鲁棒、可泛化且精确的多边形建筑足迹方法的潜力。为促进进一步研究，我们将在https://github.com/likaiucas/PolyFootNet发布偏移预测模块的预训练权重。
英文摘要：
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 post-processing 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 paper introduces Polygonal Footprint Network (PolyFootNet), a novel deep-learning framework that directly outputs polygonal building footprints without requiring external post-processing steps. 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 PolyFootNet is introducing the Self Offset Attention 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 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 building footprint extraction method from challenging off-nadir imagery. To facilitate further research, we will release pre-trained weights of our offset prediction module at https://github.com/likaiucas/PolyFootNet.
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