Multi-Task Learning for Segmentation of Building Footprints with Deep Neural Networks
Journal
International Conference of Representation Learning (ICLR) - AI for Social Good Workshop
Type
conference paper
Date Issued
2019-03-23
Author(s)
Research Team
AIML Lab
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.
Language
English
HSG Classification
contribution to scientific community
Subject(s)
Division(s)
References
https://arxiv.org/pdf/1709.05932.pdf
Eprints ID
258198
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48_aisg_iclr2019.pdf
Size
1.72 MB
Format
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