[RLF17] Deep Learning for Urban Remote Sensing

Conférence Internationale avec comité de lecture : Joint Urban Remote Sesing Event (JURSE 2017), March 2017, pp.-, Dubai,

Mots clés: Deep Learning; Remote Sensing Images; Deformable Models

Résumé: 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.

Collaboration: ONERA - DTIM


@inproceedings {
title="{Deep Learning for Urban Remote Sensing }",
author=" H. Randrianarivo and B. Le Saux and M. Ferecatu ",
booktitle="{Joint Urban Remote Sesing Event (JURSE 2017)}",
address=" Dubai",