| 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 |
Bischke, Benjamin; Helber, Patrick; Folz, Joachim; Dengel, Andreas & Borth, Damian |
| Research Team |
AIML Lab |
| 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 |
20 Jul 2022 17:39 |
| URI: |
https://www.alexandria.unisg.ch/publications/258198 |
Multi-Task Learning for Segmentation of Building Footprints with Deep Neural Networks
![[img]](http://www.alexandria.unisg.ch/style/images/fileicons/oa_orange.png)
