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 metadatas

Cited literature [14 references]  Display  Hide  Download

https://hal-lirmm.ccsd.cnrs.fr/lirmm-02087761
Contributor : Marc Chaumont <>
Submitted on : Tuesday, April 2, 2019 - 12:39:16 PM
Last modification on : Wednesday, September 11, 2019 - 1:14:10 AM
Long-term archiving on: Wednesday, July 3, 2019 - 5:28:10 PM

File

JURSE-2019-ZEGAOUI-CHAUMONT-SU...
Publisher files allowed on an open archive

Identifiers

  • HAL Id : lirmm-02087761, version 1

Citation

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⟩

Share

Metrics

Record views

449

Files downloads

297