  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.
However, at the same time this raises a set of new challenges for existing
pixel-based prediction methods, such as semantic segmentation approaches. While
deep neural networks have achieved significant advances in the semantic
segmentation of high resolution images in the past, 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 new cascaded multi-task loss. We
evaluate our approach on Inria Aerial Image Labeling Dataset which contains
large-scale and high resolution images. Our results show that we are able to
outperform state-of-the-art methods by 8.3\% without any additional
post-processing step.
