TAL: Topography-Aware Multi-Resolution Fusion Learning for Enhanced Building Footprint Extraction

Automatic building footprint extraction from remote sensing imagery is a challenging task with important applications in geomatics and environmental science. Significant advances have been made in this field as a result of the emergence of deep convolutional neural networks (CNNs) designed for semantic segmentation. Although CNNs have demonstrated state-of-the-art performance in coarse annotation and identification of buildings, the accuracy of extracted building footprints is still insufficient for high-precision applications such as mapping and navigation. We propose the topography-aware multi-resolution fusion learning strategy tailored to the problem of enhanced building footprint extraction. More specifically, we introduce a topography-aware loss (TAL) for enhancing a deep CNN’s ability to learn heterogeneous building features for better boundary preservation during segmentation. We then incorporate the proposed TAL loss within a multi-resolution fusion architecture to boost high-resolution segmentation performance. Finally, we introduce a novel metric named average thresholded contour accuracy (tCA) which specifically measures the accuracy of segmentation boundaries. The experimental results on the SpaceNet buildings dataset show significant improvements in boundary integrity of extracted building footprints when compared with previously proposed methods. Hence, this method enables accurate boundary annotation toward automatic production of building footprint maps for high-precision applications.