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Conference Papers Year : 2019

Urban object classification with 3D Deep-Learning

Younes Zegaoui
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Marc Chaumont
Gérard Subsol
Mustapha Derras
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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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Dates and versions

lirmm-02087761 , version 1 (02-04-2019)

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Younes Zegaoui, Marc Chaumont, Gérard Subsol, Philippe Borianne, Mustapha Derras. Urban object classification with 3D Deep-Learning. JURSE 2019 - Joint Urban Remote Sensing Event, May 2019, Vannes, France. ⟨10.1109/JURSE.2019.8809043⟩. ⟨lirmm-02087761⟩
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