Semantics and Contour Based Interactive Learning Network for Building Footprint Extraction

Building footprint extraction plays an important role in the analysis of remote-sensing images and has an extensive range of applications. Obtaining precise boundaries of buildings remains a challenge in existing building extraction methods. Some previous works have made notable efforts to address this concern. However, most of these methods require cumbersome and expensive postprocessing steps. Moreover, they ignored the correlation between building semantics and contours, which we believe is crucial for building footprint extraction. To mitigate this issue, this article presents an intuitive and effective framework that explores semantic and contour cues of buildings and fully excavates their correlation. Specifically, we construct an interactive dual-stream decoder. The intermediate connections within this decoder interactively transmit features between branches, contributing to learning correlations between semantics and contours. We propose the semantic collaboration module (SCM) to strengthen the connection between the two branches. To further boost performance, we build the multiscale semantic context fusion module (MSCF) to fuse semantic information from the higher and lower layers of the network, allowing the network to obtain superior feature representations. The experiment results on the WHU, INRIA, and Massachusetts building datasets demonstrate the superior performance of our method.