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S. Clode, F. Rottensteiner, P. Kootsookos:
"Improving City Model Determination by Using Road Detection from LIDAR Data";
Poster: Joint Workshop of ISPRS and DAGM, CMRT05, Vienna; 2005-08-29 - 2005-08-30; in: "Object Extraction for 3D City Models, Road Databases, and Traffic Monitoring - Concepts, Algorithms, and Evaluation", U. Stilla, F. Rottensteiner, S. Hinz (ed.); The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVI - part 3/W24 (2005), ISSN: 1682-1750; 159 - 164.

A new road classification technique from LIght Detection And Ranging (LIDAR) data is presented that relies on region growing in order to classify areas as road. The new method corrects some of the problems encountered with previously documented LIDAR road detectors. A major benefit of the new road detection method is that it can be combined with standard building detection techniques to detect bridges within the road network. As a consequence bridges are identified as false positive detections in the candidate building regions and can be removed, thus improving the obtained building mask whilst more detail is added to the final classification scheme seen in the road network. Vectorisation of the detected road network is performed using a Phase Coded Disk (PCD) thus completing the detection and vectorisation processes. The benefits of using LIDAR data in road extraction is emphasised by the simple but automated creation of longitudinal profiles and cross sections from the vectorised road network.

http://aleph.ub.tuwien.ac.at/F?base=tuw01&func=find-c&ccl_term=AC05936037

http://publik.tuwien.ac.at/files/PubDat_120014.pdf