Three-Dimensional LiDAR Data Classifying to Extract Road Point in Urban Area

The Light Detection and Ranging (LiDAR) system is one of the best ways to accurately and effectively gather 3-D terrain information. However, it is complicated to process the LiDAR cloud data due to its irregularity and large number of collected data points. This letter proposes a novel method to automatically extract urban road network from 3-D LiDAR data. This method uses height and reflectance of LiDAR data, and clustered road point information. Geometric information of general roads is also applied to correctly extract road points group. The proposed method has been tested on various urban areas which contain complicated road networks. The results demonstrate that the integration of height, reflectance, and geometric information of roads is a crucial factor that distinguishes the proposed method in its ability to reliably and correctly classify road points.