Skip to Main content Skip to Navigation
Conference papers

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.
Complete list of metadata

Cited literature [14 references]  Display  Hide  Download
Contributor : Marc Chaumont Connect in order to contact the contributor
Submitted on : Tuesday, April 2, 2019 - 12:39:16 PM
Last modification on : Friday, August 5, 2022 - 3:02:19 PM
Long-term archiving on: : Wednesday, July 3, 2019 - 5:28:10 PM


Publisher files allowed on an open archive



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⟩



Record views


Files downloads