Multi-Task Learning for Segmentation of Building Footprints with Deep Neural Networks

Item Type Conference or Workshop Item (Paper)
Abstract

The increased availability of high-resolution satellite imagery allows to sense very detailed structures on the surface of our planet and opens up new direc- tions in the analysis of remotely sensed 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 seg- mentation boundaries in high-resolution satellite imagery by introducing a novel multi-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 additional post-processing steps. Source code and all models are available under https: //github.com/bbischke/MultiTaskBuildingSegmentation.

Authors Benjamin, Bischke; Patrick, Helber; Joachim, Folz; Andreas, Dengel & Damian, Borth
Journal or Publication Title International Conference of Representation Learning (ICLR) - AI for Social Good Workshop
Language English
Subjects computer science
HSG Classification contribution to scientific community
Date 23 March 2019
References https://arxiv.org/pdf/1709.05932.pdf
Depositing User Prof. Dr. Damian Borth
Date Deposited 26 Oct 2019 21:17
Last Modified 04 Nov 2019 06:51
URI: https://www.alexandria.unisg.ch/publications/258198

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Benjamin, Bischke; Patrick, Helber; Joachim, Folz; Andreas, Dengel & Damian, Borth: Multi-Task Learning for Segmentation of Building Footprints with Deep Neural Networks. 2019.

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https://www.alexandria.unisg.ch/id/eprint/258198
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