BiSVP: Building Footprint Extraction via Bidirectional Serialized Vertex Prediction
This addresses the problem of efficient and accurate building instance segmentation for remote sensing applications, representing an incremental improvement over existing methods.
The paper tackles building footprint extraction from remote sensing images by introducing BiSVP, a refinement-free end-to-end method that predicts serialized vertices bidirectionally, achieving state-of-the-art performance on three benchmarks with significant margins.
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.