A Concentric Loop Convolutional Neural Network for Manual Delineation-Level Building Boundary Segmentation From Remote-Sensing Images

Shiqing Wei, Tao Zhang, Shunping Ji

Abstract: To date, accurate building footprint delineation in the surveying, mapping, and geographic information system (GIS) communities has been dependent on human labor. In this article, to address this issue, we propose a concentric loop convolutional neural network (CLP-CNN) method for the automatic segmentation of building boundaries from remote-sensing images. The proposed method consists of three components: 1) a boundary detector to extract coarse polygonal boundaries of individual regions of interest; 2) a concentric loop-shaped convolutional network with bidirectional pairing loss to fine-tune the vertices of the polygons; and 3) a refinement block, which removes redundant vertices and regularizes the boundaries to polygons at the manual delineation level. We also demonstrate that the proposed CLP-CNN method is applicable to other generic objects in natural images. Experiments on two building datasets confirmed that more than 77%/67% of the building polygons predicted by the proposed method are on par with the manual delineation level, representing a significant saving in the labor cost of manual annotation. In generic object boundary delineation tests performed on the Semantic Boundaries Dataset (SBD), the proposed method outperformed the most recent state-of-the-art methods by at least 3.1% in average precision (AP). Furthermore, compared with other vertex matching methods, the learning process of the proposed method converges faster. The source code will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">http://gpcv.whu.edu.cn/data</uri> .