MULTI-TASK LEARNING FOR SEGMENTATION OF BUILDING FOOTPRINTS WITH DEEP NEURAL NETWORKS

The increased availability of high-resolution satellite imagery allows to sense very detailed structures on the surface of our planet. Access to such information opens up new directions in the analysis of remote sensing imagery. While deep neural networks have achieved significant advances in semantic segmentation of high-resolution images, most of the existing approaches tend to produce predictions with poor boundaries. In this paper, we address the problem of preserving semantic segmentation boundaries in high-resolution satellite imagery by introducing a novel multi-task loss. The loss leverages multiple output representations of the segmentation mask and biases the network to focus more on pixels near boundaries. We evaluate our approach on the large-scale Inria Aerial Image Labeling Dataset which contains high-resolution images. Our results show that we are able to outperform state-of-the-art methods by 9.8% on the Intersection over Union (IoU) metric without any additional post-processing steps.