Publication

Multi-task learning for segmentation of building footprints with neural networks

Benjamin Bischke, Patrick Helber, Joachim Folz, Damian Borth, Andreas Dengel

In: AI4SocialGood. International Conference on Learning Representations (ICLR-2019) May 6-9 New Orleans Louisiana United States arXiv 2019.

Abstract

The increased availability of high-resolution satellite imagery allows to sensevery detailed structures on the surface of our planet and opens up new direc-tions in the analysis of remotely sensed imagery. While deep neural networkshave achieved significant advances in semantic segmentation of high-resolutionimages, most of the existing approaches tend to produce predictions with poorboundaries. In this paper, we address the problem of preserving semantic seg-mentation boundaries in high-resolution satellite imagery by introducing a novelmulti-task loss. The loss leverages multiple output representations of the seg-mentation mask and biases the network to focus more on pixels near bound-aries. We evaluate our approach on the large-scale Inria Aerial Image Label-ing Dataset. Our results outperform existing methods with the same architec-ture by about 3% on the Intersection over Union (IoU) metric without additionalpost-processing steps. Source code and all models are available under https://github.com/bbischke/MultiTaskBuildingSegmentation.

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz