A Large-Scale Mapping Scheme for Urban Building From Gaofen-2 Images Using Deep Learning and Hierarchical Approach

Urban buildings are essential components of cities and an indispensable source of urban geographic information. While there are many research efforts focused on urban buildings extraction, there are few studies on large-scale urban building mapping based on satellite images. In this research, a large-scale urban building mapping scheme based on Gaofen-2 satellite (GF-2) images is proposed based on a hierarchical approach. In this hierarchical approach, urban buildings are regarded as a mixture of dense low-rise buildings (DLBs) and sparse independent buildings (SIBs) stacked in space, which are extracted by a semantic segmentation model and an instance segmentation model, respectively. In this study, GF-2 images and OpenStreetMap data were used to extract DLB using
$U^2$
-Net with focal loss. GF-2 images were used to extract SIB using an improved CenterMask model with a deformable convolution network and a spatial coordinate attention module. The main urban area within the 5th ring road of Beijing was selected as the study area. With the trained model, the GF-2 image tiles of Beijing input into the models to first derive coarse maps of DLB and SIB. Postprocessing optimization was performed after combining the maps. The accuracy assessment shows that the overall accuracy of large-scale urban building mapping using the hierarchical approach proposed in this article reaches 91.5%, which is 4.8% higher than that with a traditional method. Overall, the hierarchical approach proposed in this article is effective in large-scale urban building mapping and provides new application opportunities.