Urban object classification with 3D Deep-Learning

Zegaoui Younes, Chaumont Marc, Subsol Gérard, Borianne Philippe, Derras Mustapha. 2019. Urban object classification with 3D Deep-Learning. In : 2019 Joint Urban Remote Sensing Event (JURSE 2019): Proceedings of a meeting held 22-24 May 2019, Vannes, France. IEEE-GRSS. Piscataway : IEEE, 4 p. ISBN 978-1-7281-0009-8 International Joint Urban Remote Sensing Event (JURSE 2019), Vannes, France, 22 Mai 2019/24 Mai 2019.

Résumé : 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.

Auteurs et affiliations

• Zegaoui Younes, LIRMM (FRA)
• Chaumont Marc, LIRMM (FRA)
• Subsol Gérard, LIRMM (FRA)
• Borianne Philippe, CIRAD-BIOS-UMR AMAP (FRA)
• Derras Mustapha, Berger-Levrault (FRA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/600783/)

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