Detection and vectorization of roads from lidar data
Date
2007
Authors
Clode, S.
Rottensteiner, F.
Kootsookos, P.
Zelniker, Emanuel Emil
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
Photogrammetric Engineering and Remote Sensing, 2007; 73 (5):517-535
Statement of Responsibility
Simon Clode, Franz Rottensteiner, Peter Kootsookos, and Emanuel Zelniker
Conference Name
Abstract
A method for the automatic detection and vectorization of roads from lidar data is presented. To extract roads from a lidar point cloud, a hierarchical classification technique is used to classify the lidar points progressively into road and non-road points. During the classification process, both intensity and height values are initially used. Due to the homogeneous and consistent nature of roads, a local point density is introduced to finalize the classification. The resultant binary classification is then vectorized by convolving a complex-valued disk named the Phase Coded Disk (PCD) with the image to provide three separate pieces of information about the road. The centerline and width of the road are obtained from the resultant magnitude image while the direction is determined from the corresponding phase image, thus completing the vectorized road model. All algorithms used are described and applied to two urban test sites. Completeness values of 0.88 and 0.79 and correctness values of 0.67 and 0.80 were achieved for the classification phase of the process. The vectorization of the classified results yielded RMS values of 1.56 m and 1.66 m, completeness values of 0.84 and 0.81 and correctness values of 0.75 and 0.80 for two different data sets.
School/Discipline
School of Computer Science
Dissertation Note
Provenance
Description
Copyright © 2006 ASPRS