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Deep learning for urban remote sensing
Abstract : This work shows how deep learning techniques can benefit to remote sensing. We focus on tasks which are recurrent in Earth Observation data analysis. For classification and semantic mapping of aerial images, we present various deep network architectures and show that context information and dense labeling allow to reach better performances. For estimation of normals in point clouds, combining Hough transform with convolutional networks also improves the accuracy of previous frameworks by detecting hard configurations like corners. It shows that deep learning allows to revisit remote sensing and offers promising paths for urban modeling and monitoring.
Cited literature [21 references]
https://hal.archives-ouvertes.fr/hal-01672854
Contributor : Sébastien Lefèvre Connect in order to contact the contributor
Submitted on : Monday, March 26, 2018 - 1:29:29 PM
Last modification on : Monday, February 21, 2022 - 3:38:09 PM
Long-term archiving on: : Thursday, September 13, 2018 - 10:12:50 AM
Contributor : Sébastien Lefèvre Connect in order to contact the contributor
Submitted on : Monday, March 26, 2018 - 1:29:29 PM
Last modification on : Monday, February 21, 2022 - 3:38:09 PM
Long-term archiving on: : Thursday, September 13, 2018 - 10:12:50 AM
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JURSE-2017-Audebert-et-al.pdf
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