  Despite recent advances in deep-learning based semantic segmentation,
automatic building detection from remotely sensed imagery is still a
challenging problem owing to large variability in the appearance of buildings
across the globe. The errors occur mostly around the boundaries of the building
footprints, in shadow areas, and when detecting buildings whose exterior
surfaces have reflectivity properties that are very similar to those of the
surrounding regions. To overcome these problems, we propose a generative
adversarial network based segmentation framework with uncertainty attention
unit and refinement module embedded in the generator. The refinement module,
composed of edge and reverse attention units, is designed to refine the
predicted building map. The edge attention enhances the boundary features to
estimate building boundaries with greater precision, and the reverse attention
allows the network to explore the features missing in the previously estimated
regions. The uncertainty attention unit assists the network in resolving
uncertainties in classification. As a measure of the power of our approach, as
of December 4, 2021, it ranks at the second place on DeepGlobe's public
leaderboard despite the fact that main focus of our approach -- refinement of
the building edges -- does not align exactly with the metrics used for
leaderboard rankings. Our overall F1-score on DeepGlobe's challenging dataset
is 0.745. We also report improvements on the previous-best results for the
challenging INRIA Validation Dataset for which our network achieves an overall
IoU of 81.28% and an overall accuracy of 97.03%. Along the same lines, for the
official INRIA Test Dataset, our network scores 77.86% and 96.41% in overall
IoU and accuracy.
