Skeleton-based automatic road network extraction from an orthophoto colored point cloud

Abstract

Reliable and up-to-date road network information is crucial to guarantee efficient logistic distribution, emergency response, urban planning, etc. Road networks in developing urban areas tend to change rapidly. Periodic remapping is necessary to maintain the temporal quality of the road network information. Updating the road network using conventional methods can be a tedious task. This paper presents a methodology to extract road network automatically from an airborne LiDAR point cloud combined with color information from an aerial orthophoto. First, ground points are separated from non-ground points. We then classify the filtered ground points to road and non-road points using the Random Forest (RF) algorithm. Parallel thinning, method for skeletonization of the road segment, is carried out on a binary image extracted by a so-called density map of the classified road points. Finally, road centerline is obtained by our proposed topological order and regularization approach. The proposed method is tested on ISPRS benchmark data of Vaihingen - Germany. Skeleton-based road network extraction is a promising method as more than 95% roads in the study area are extracted. In the future, regularization of the skeleton to obtain smoother line representation is still an essential but challenging research.