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:从遥感图像中提取建筑脚印最近引起了广泛的关注。 大量的方法通过产生具有累赘的精细阶段的矢量化建筑面罩来应对这一具有挑战性的问题,这限制了这些方法的应用。 在本文中,我们引入了一种新的无精细和端到端的建筑脚印提取方法,这种方法在概念上是直观的、简单和有效的。我们称为BisVP的方法是一个带有定购的脊椎的建筑样板,并将建筑脚印制成以双向方式直接预测串联的脊椎。此外,我们提议了一个跨尺度的特征集成模块,以促进高分辨率和丰富的语义特征学习,这对于密集的建筑顶端预测任务至关重要。没有钟声和哨声,我们的BisVP在三个建案分级基准上以相当大的边际优劣优劣优。 代码和数据集将被公诸于众。</s>