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.
Type de document :
Communication dans un congrès
Joint Urban Remote Sensing Event (JURSE), Mar 2017, Dubai, United Arab Emirates. 2017 Joint Urban Remote Sensing Event (JURSE), 2017, 〈10.1109/JURSE.2017.7924536〉
Liste complète des métadonnées

Littérature citée [21 références]  Voir  Masquer  Télécharger

https://hal.archives-ouvertes.fr/hal-01672854
Contributeur : Sébastien Lefèvre <>
Soumis le : lundi 26 mars 2018 - 13:29:29
Dernière modification le : mardi 10 juillet 2018 - 17:02:04

Fichier

JURSE-2017-Audebert-et-al.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Citation

Nicolas Audebert, Alexandre Boulch, Hicham Randrianarivo, Bertrand Le Saux, Marin Ferecatu, et al.. Deep learning for urban remote sensing. Joint Urban Remote Sensing Event (JURSE), Mar 2017, Dubai, United Arab Emirates. 2017 Joint Urban Remote Sensing Event (JURSE), 2017, 〈10.1109/JURSE.2017.7924536〉. 〈hal-01672854〉

Partager

Métriques

Consultations de la notice

648

Téléchargements de fichiers

82