Road detection from airborne LiDAR point clouds adaptive for variability of intensity data

This paper presents a novel algorithm of road detection from airborne LiDAR point clouds adaptive for variability of intensity data of road network. First, the point cloud topology is constructed using a grid index structure which facilitate spatial searching and preserves the accuracy of raw data without interpolation, and a LiDAR filtering algorithm is employed to distinguish the ground points from non-ground points. Second, road candidates are identified in the derived ground points by segmentation based on local intensity distribution histogram. Finally, the ultimate road point sets are verified by global inference based on the roughness and area of the road candidate point sets. The roughness of candidate point sets are calculated based on morphological gradients in consideration of the characteristics of roads compared to other non-road ground areas such as grass land and bare ground. The experimental results using practical data in complex environment demonstrate that this algorithm is able to automatically detect roads adaptive for the variability of intensity data of road network. Other non-road ground areas such as grass land and bare ground can be efficiently eliminated. (C) 2015 Elsevier GmbH. All rights reserved.