| 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 multi-scale 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 APmask, 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 APmask. 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 APmask score of 60.4%, which demonstrated that it’s capable of extracting complicated buildings, such as high-rise buildings and villas and factories of multiple shapes, even though the images were off-nadir. © 1980-2012 IEEE. |
