Name
Abstract | ||
|---|---|---|
This paper presents a workflow including a novel algorithm for road detection from dense LiDAR fused with high-resolution aerial imagery data. Using a supervised machine learning approach point clouds are firstly classified into one of three groups: building, ground, or unassigned. Ground points are further processed by a novel algorithm to extract a road network. The algorithm exploits the high variance of slope and height of the point data in the direction orthogonal to the road boundaries. Applying the proposed approach on a 40 million point dataset successfully extracted a complex road network with an F-measure of 76.9%. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/IGARSS.2015.7326746 | 2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) |
Keywords | Field | DocType |
aerial laser scanning, aerial imagery, data fusion, road detection, machine learning, hybrid indexing | Computer vision,Laser scanning,Computer science,Remote sensing,Sensor fusion,Lidar,Artificial intelligence,Statistical classification,Point cloud,Aerial imagery,Workflow | Conference |
ISSN | Citations | PageRank |
2153-6996 | 0 | 0.34 |
References | Authors | |
2 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anh-Vu Vo | 1 | 0 | 1.69 |
| Linh Truong-Hong | 2 | 43 | 4.79 |
| Debra F. Laefer | 3 | 54 | 7.45 |