Urban object classification with 3D Deep-Learning

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.

Bibliographic Details

Main Authors:	Zegaoui, Younes, Chaumont, Marc, Subsol, Gérard, Borianne, Philippe, Derras, Mustapha
Format:	conference_item biblioteca
Language:	eng
Published:	IEEE
Online Access:	http://agritrop.cirad.fr/600783/ http://agritrop.cirad.fr/600783/1/ID600783.pdf
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