Automatic Road Extraction from Dense Urban Area by Integrated Processing of High Resolution Imagery and Lidar Data

Automated and reliable 3D city model acquisition is an increasing demand. Automatic road extraction from dense urban areas is a challenging issue due to the high complex image scene. From imagery, the obstacles of the extraction stem mainly from the difficulty of finding clues of the roads and complexity of the contextual environments. One of the promising methods to deal with this is to use data sources from multi-sensors, by which the multiple clues and constraints can be obtained so that the uncertainty can be minimized significantly. This paper focuses on the integrated processing of high resolution imagery and LIDAR (LIght Detection And Ranging) data for automatic extraction of grid structured urban road network. Under the guidance of an explicit model of the urban roads in a grid structure, the method firstly detects the primitives or clues of the roads and the contextual targets (i.e., parking lots, grasslands) both from the color image and lidar data by segmentation and image analysis. Evidences of road existing are contained in the primitives. The candidate road stripes are detected by an iterative Hough transform algorithm. This is followed by an procedure of evidence finding and validation by taking advantage of high resolution imagery and direct height information of the scene derived from lidar data. Finally the road network is formed by topology analysis. In this paper, the strategy and corresponding algorithms are described. The test data set is color ortho-imagery with 0.5 m resolution and lidar data of Toronto downtown area. The experimental results in the typical dense urban scene indicate it is able to extract the roads much more reliable and accurate by the integrated processing than by using imagery or lidar separately. It saliently exhibits advantages of the integrated processing of the multiple data sources for the road extraction from the complicated scenes.