Graph Convolutional Networks for the Automated Production of Building Vector Maps From Aerial Images

Building footprint delineation from remote sensing imagery is a basic task in surveying and mapping and geographic information system (GIS), which benefits many engineering applications but requires an enormous amount of human delineation. In this study, we aim to find a way to replace human delineation with automated algorithms. For this purpose, we designed a novel pipeline for the automated production of building vector maps from aerial images, which consists of a bounding-box generation module, a graph convolutional network (GCN)-based polygon prediction module, and an empirical polygon regularization module. First, we introduce the bounding box generation module based on region-based object detection, which along with our overlap cropping strategy is used to generate a bounding box for each building instance. The generated bounding boxes have a horizontal rectangle version and a rotated version, which are used to initialize the next stage of our method. Second, we propose a GCN-based method, which is the core of this study, to conduct the initial accurate building polygon prediction by integrating multiresolution optimization and multilevel loss constraints. Our proposed method, to the authors’ best knowledge, is the first of its kind that introduces GCN to building extraction. The final step is to apply a regularization algorithm to translate the predicted polygons into structured and highly accurate building footprints. We validated the proposed method on two large aerial building data sets, WHU data set and Inria data set, where it was shown to significantly outperform other state-of-the-art methods more than 10%. Specifically, with the ground-truth rotated bounding boxes of buildings, our method is able to automatically delineate 91% of the buildings in the WHU data set and with the predicted rotated bounding the percent reached human-level delineation.