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.

Citation

@inproceedings{audebert2017deep,
  title={Deep learning for urban remote sensing},
  author={Audebert, Nicolas and Boulch, Alexandre and Randrianarivo, Hicham and Le Saux, Bertrand and Ferecatu, Marin and Lefevre, S{\'e}bastien and Marlet, Renaud},
  booktitle={2017 Joint Urban Remote Sensing Event (JURSE)},
  pages={1--4},
  year={2017},
  organization={IEEE}
}