A method for main road extraction from airborne LiDAR data in urban area

2011 ◽  
Author(s):  
Peng Jiangui ◽  
Gao Guang
Keyword(s):  
Urban Area ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Road Extraction ◽  
Main Road ◽  
Airborne Lidar Data

Assessment of seismic loading on structures based on airborne LiDAR data from the Kalochori urban area (N. Greece)

2016 ◽  
Author(s):  
Emmanouil Rovithis ◽  
Emmanouil Kirtas ◽  
Eleftheria Marini ◽  
Dimitris Bliziotis ◽  
Evangelos Maltezos ◽  
...  
Keyword(s):  
Urban Area ◽  
Seismic Loading ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Airborne Lidar Data

Automatic road extraction for airborne lidar data

2013 ◽  
Author(s):  
Yuan Wang ◽  
Siying Chen ◽  
Yinchao Zhang ◽  
He Chen ◽  
Pan Guo ◽  
...  
Keyword(s):  
Airborne Lidar ◽  
Lidar Data ◽  
Road Extraction ◽  
Airborne Lidar Data

scholarly journals Automatic Classification of coarse density LiDAR data in urban area

2014 ◽  
Vol XL-5 ◽  
pp. 77-81
Author(s):  
H.M. Badawy ◽  
A. Moussa ◽  
N. El-Sheimy
Keyword(s):  
Urban Area ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Low Density ◽  
Intensity Analysis ◽  
Challenging Problem ◽  
Density Data ◽  
Airborne Lidar Data

The classification of different objects in the urban area using airborne LIDAR point clouds is a challenging problem especially with low density data. This problem is even more complicated if RGB information is not available with the point clouds. The aim of this paper is to present a framework for the classification of the low density LIDAR data in urban area with the objective to identify buildings, vehicles, trees and roads, without the use of RGB information. The approach is based on several steps, from the extraction of above the ground objects, classification using PCA, computing the NDSM and intensity analysis, for which a correction strategy was developed. The airborne LIDAR data used to test the research framework are of low density (1.41 pts/m<sup>2</sup>) and were taken over an urban area in San Diego, California, USA. The results showed that the proposed framework is efficient and robust for the classification of objects.

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