Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps

Nicolas Audebert 1, 2 Bertrand Le Saux 1 Sébastien Lefèvre 2
2 OBELIX - Environment observation with complex imagery
UBS - Université de Bretagne Sud, IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : In this work, we investigate the use of OpenStreetMap data for semantic labeling of Earth Observation images. Deep neural networks have been used in the past for remote sensing data classification from various sensors, including multispectral, hyperspectral, SAR and LiDAR data. While OpenStreetMap has already been used as ground truth data for training such networks, this abundant data source remains rarely exploited as an input information layer. In this paper, we study different use cases and deep network architectures to leverage OpenStreetMap data for semantic labeling of aerial and satellite images. Especially , we look into fusion based architectures and coarse-to-fine segmentation to include the OpenStreetMap layer into multispectral-based deep fully convolutional networks. We illustrate how these methods can be successfully used on two public datasets: ISPRS Potsdam and DFC2017. We show that OpenStreetMap data can efficiently be integrated into the vision-based deep learning models and that it significantly improves both the accuracy performance and the convergence speed of the networks.
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Communication dans un congrès
EARTHVISION 2017 IEEE/ISPRS CVPR Workshop. Large Scale Computer Vision for Remote Sensing Imagery, Jul 2017, Honolulu, United States. 2017
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Contributeur : Nicolas Audebert <>
Soumis le : mardi 16 mai 2017 - 15:02:04
Dernière modification le : mercredi 2 août 2017 - 10:10:37

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  • HAL Id : hal-01523573, version 1
  • ARXIV : 1705.06057

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Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre. Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps. EARTHVISION 2017 IEEE/ISPRS CVPR Workshop. Large Scale Computer Vision for Remote Sensing Imagery, Jul 2017, Honolulu, United States. 2017. <hal-01523573>

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