Multitask Learning of Height and Semantics from Aerial Images

Marcela Carvalho; Bertrand Le Saux; Pauline Trouve-Peloux; Frederic Champagnat; Andres Almansa

doi:10.1109/LGRS.2019.2947783

Multitask Learning of Height and Semantics from Aerial Images

Marcela Carvalho
, Bertrand Le Saux
, Pauline Trouve-Peloux
, Frederic Champagnat
, Andres Almansa

Université Paris-Saclay
Laboratoire de Probabilités et Modèles Aléatoires

Résultats de recherche: Contribution à un journal › Article › Revue par des pairs

64 Citations (Scopus)

Résumé

Aerial or satellite imagery is a great source for land surface analysis, which might yield land-use maps or elevation models. In this letter, we present a neural network framework for learning semantics and local height together. We show how this joint multitask learning benefits to each task on the large data set of the 2018 Data Fusion Contest. Moreover, our framework also yields an uncertainty map that allows assessing the prediction of the model. Code is available at http://github.com/marcelampc/mtl_aerial_images

langue originale	Anglais
Numéro d'article	8891800
Pages (de - à)	1391-1395
Nombre de pages	5
journal	IEEE Geoscience and Remote Sensing Letters
Volume	17
Numéro de publication	8
Les DOIs	https://doi.org/10.1109/LGRS.2019.2947783
état	Publié - 1 août 2020
Modification externe	Oui

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abstract = "Aerial or satellite imagery is a great source for land surface analysis, which might yield land-use maps or elevation models. In this letter, we present a neural network framework for learning semantics and local height together. We show how this joint multitask learning benefits to each task on the large data set of the 2018 Data Fusion Contest. Moreover, our framework also yields an uncertainty map that allows assessing the prediction of the model. Code is available at http://github.com/marcelampc/mtl\_aerial\_images",

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AU - Le Saux, Bertrand

AU - Trouve-Peloux, Pauline

AU - Champagnat, Frederic

AU - Almansa, Andres

PY - 2020/8/1

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AB - Aerial or satellite imagery is a great source for land surface analysis, which might yield land-use maps or elevation models. In this letter, we present a neural network framework for learning semantics and local height together. We show how this joint multitask learning benefits to each task on the large data set of the 2018 Data Fusion Contest. Moreover, our framework also yields an uncertainty map that allows assessing the prediction of the model. Code is available at http://github.com/marcelampc/mtl_aerial_images

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KW - semantic segmentation

KW - single view depth estimation

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Multitask Learning of Height and Semantics from Aerial Images

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