Extracting building footprints from remote sensing images has been attracting extensive attention recently. Dominant approaches address this challenging problem by generating vectorized building masks with cumbersome refinement stages, which limits the application of such methods. In this paper, we introduce a new refinement-free and end-to-end building footprint extraction method, which is conceptually intuitive, simple, and effective. Our method, termed as BiSVP, represents a building instance with ordered vertices and formulates the building footprint extraction as predicting the serialized vertices directly in a bidirectional fashion. Moreover, we propose a cross-scale feature fusion (CSFF) module to facilitate high resolution and rich semantic feature learning, which is essential for the dense building vertex prediction task. Without bells and whistles, our BiSVP outperforms state-of-the-art methods by considerable margins on three building instance segmentation benchmarks, clearly demonstrating its superiority. The code and datasets will be made public available.

翻译：从遥感影像中提取建筑物轮廓近年来备受关注。主流方法通过繁琐的细化步骤生成矢量化的建筑掩膜来解决这一挑战性问题，这限制了此类方法的应用。本文提出一种无需细化、端到端的建筑物轮廓提取新方法，该方法概念直观、简洁且高效。我们将该方法命名为BiSVP，通过有序顶点表示建筑实例，并直接以双向方式预测序列化顶点来构建建筑物轮廓提取过程。此外，我们提出跨尺度特征融合（CSFF）模块，以促进高分辨率与丰富语义特征的学习，这对密集建筑顶点预测任务至关重要。无需任何附加优化手段，我们的BiSVP在三个建筑实例分割基准测试中均显著优于现有最优方法，充分证明了其优越性。相关代码与数据集将公开提供。