Poly-BRBLE: A Boundary Refinement-Based Individual Building Localization and Extraction Model Combined With Regularization

Shuwei Tang, Xue Wang, Chen Pan, Renjie Ji, Chongrong Zhou, Kun Tan

Abstract: Automatic building localization and extraction based on high-resolution remote sensing images is of great importance to city mapping and smart city management. Extracted buildings with high precisions and fine boundaries contribute to the vectorization operation and, thus, the digital line graph (DLG) production. In this regard, a comprehensive framework Poly-BRBLE is proposed, combining a boundary refinement-based individual building localization and extraction model BRBLE as well as a particularly revised regularization method. The BRBLE is mainly composed of a multiscale feature fusion and propagation module and a coarse-to-fine mask optimization module, which allow the model to identify buildings from similar backgrounds and extract them with precise boundaries. Comparisons were made between BRBLE and other classical and state-of-the-art models on the WHU building dataset, the Chinese building instance segmentation dataset, and the Inria Polygon dataset, which demonstrated that BRBLE outperformed the second-best model by 2%, 1.5%, and 1.2%, respectively, in ${\text {AP}}_{\text {mask}}$ , and the advantage was further enlarged on large objects to 5.2%, 1.8%, and 2%. Moreover, we built the SH building dataset, which contained complex-shaped buildings and mixed-built environments, where it was demonstrated that BRBLE outperformed the second-best model by 3.4% in ${\text {AP}}_{\text {mask}}$ . We further compared the precisions of the building footprints obtained by the Poly-BRBLE and other different methods, where Poly-BRBLE showed a superior performance, with an ${\text {AP}}_{\text {mask}}$ score of 60.4%, which demonstrated that it is capable of extracting complicated buildings, such as high-rise buildings and villas, and factories of multiple shapes, even though the images were off-nadir.