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The road extraction in urban areas is difficult task due to the complicated patterns and many contextual objects. LiDAR data\uddirectly provides three dimensional (3D) points with less occlusions and smaller shadows. The elevation information and surface\udroughness are distinguishing features to separate roads. However, LiDAR data has some disadvantages are not beneficial to object\udextraction, such as the irregular distribution of point clouds and lack of clear edges of roads. For these problems, this paper\udproposes an automatic road centerlines extraction method which has three major steps: (1) road center point detection based on\udmultiple feature spatial clustering for separating road points from ground points, (2) local principal component analysis with least\udsquares fitting for extracting the primitives of road centerlines, and (3) hierarchical grouping for connecting primitives into\udcomplete roads network. Compared with MTH (consist of Mean shift algorithm, Tensor voting, and Hough transform) proposed in\udour previous article, this method greatly reduced the computational cost. To evaluate the proposed method, the Vaihingen data set,\uda benchmark testing data provided by ISPRS for “Urban Classification and 3D Building Reconstruction” project, was selected. The\udexperimental results show that our method achieve the same performance by less time in road extraction using LiDAR data

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

AN EFFICIENT METHOD FOR AUTOMATIC ROAD EXTRACTION BASED ON MULTIPLE FEATURES FROM LiDAR DATA