Segmentation of Building Footprints with Xception and IoUloss

Advanced sensors are increasingly used in aeronautical facility. Broader acquisition of images improves the quality and quantity of remote sensing images and makes a large number of fine remote sensing images available. Although semantic segmentation models based on deep learning has been a great success in computer vision, there are still a lot of difficulties compared to regular visual senses. In this paper, we propose a novel end-to-end model for remote sensing image segmentation of building footprints and a loss function based on IoU to improve the performance. Semantic labels for building structures are being accurately allocated by our method. We make control experiments on Map Challenge dataset with other semantic segmentation model based on recent deep learning advances. Our proposed method generates 94.73% of IoU on the Map Challenge Dataset's validation set, which outperforms all previous models in literature.