Enhancing Building Footprint Extraction With Partial Occlusion by Exploring Building Integrity

Building footprint extraction (BFE) is essential for applications like land use management, urban planning, and database updates. However, many existing methods struggle with occluded buildings in remote sensing images, causing performance degradation. In addition, methods designed for occluded buildings often depend on external data, such as LiDAR or multiview images, increasing extraction costs. To address this challenge, we present a robust building extraction framework that effectively targets occluded buildings. Specifically, we design a low-cost image enhancement strategy. This strategy leverages the segmented labels to introduce object information about the buildings, aiding in generating enhanced images with strong building integrity, no occlusions, and high contrast with the background. Then, a building integrity enhancement branch (BIEB) is proposed. This branch incorporates enhanced building images as supervision, in order to leverage building integrity to mitigate the impact of occlusion. Finally, we introduce the idea of residual learning. This approach facilitates the model’s ability to leverage building integrity to mitigate the impact of occlusion. Extensive experiments on the WHU, Massachusetts, Inria, and Aerial Imagery for Roof Segmentation (AIRS) datasets have demonstrated the effectiveness of our approach in extracting occluded buildings. The experimental results show that, compared with the latest HD-Net, our BIENet achieved IoU score improvements of 1.49%, 1.16%, and 2.26%, and $F1$ score improvements of 0.82%, 0.70%, and 1.22%, respectively, on the WHU, Inria, and AIRS datasets. The source code of the proposed BIENet is available at https://github.com/tqwhdx19/bienet.