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Communication dans un congrès

Urban object classification with 3D Deep-Learning

Abstract : Automatic urban object detection remains a challenge for city management. Existing approaches in remote sensing include the use of aerial images or LiDAR to map a scene. This is, for example, the case for patch-based detection methods. However, these methods do not fully exploit the 3D information given by a LiDAR acquisition because they are similar to depth map. 3D Deep-Learning methods are promising to tackle the issue of the urban objects detection inside a LiDAR cloud. In this paper, we present the results of several experiments on urban object classification with the PointNet network trained with public data and tested on our data-set. We show that such a methodology delivers encouraging results, and also identify the limits and the possible improvements.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-02087761
Contributeur : Marc Chaumont <>
Soumis le : mardi 2 avril 2019 - 12:39:16
Dernière modification le : jeudi 2 juillet 2020 - 14:01:13
Archivage à long terme le : : mercredi 3 juillet 2019 - 17:28:10

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JURSE-2019-ZEGAOUI-CHAUMONT-SU...
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  • HAL Id : lirmm-02087761, version 1

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Younes Zegaoui, Marc Chaumont, Gérard Subsol, Philippe Borianne, Mustapha Derras. Urban object classification with 3D Deep-Learning. JURSE: Joint Urban Remote Sensing Event, May 2019, Vannes, France. ⟨lirmm-02087761⟩

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