Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/JURSE.2019.8809043 | 2019 Joint Urban Remote Sensing Event (JURSE) |
Keywords | DocType | ISSN |
LiDAR,deep-learning,3D points cloud,urban objects,remote sensing,classification | Conference | 2334-0932 |
ISBN | Citations | PageRank |
978-1-7281-0010-4 | 0 | 0.34 |
References | Authors | |
4 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Younes Zegaoui | 1 | 0 | 0.34 |
| Marc Chaumont | 2 | 172 | 20.40 |
| Gérard Subsol | 3 | 393 | 84.30 |
| Philippe Borianne | 4 | 0 | 0.34 |
| Mustapha Derras | 5 | 9 | 9.98 |