The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1135–1140, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1135-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1135–1140, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1135-2019

  05 Jun 2019

05 Jun 2019

AUTOMATIC DETECTION OF ROAD EDGES FROM AERIAL LASER SCANNING DATA

L. Truong-Hong1, D. F. Laefer2, and R. C. Lindenbergh1 L. Truong-Hong et al.
  • 1Dept. of Geoscience & Remote Sensing, Delft University of Technology, Delft, The Netherland
  • 2Center for Urban Science and Progress, New York University, New York, USA

Keywords: Road Curb, Road Extraction, Aerial Laser Scanning, Cell Decomposition, Cell-based Region Growing

Abstract. When aerial laser scanning (ALS) is deployed with targeted flight path planning, urban scenes can be captured in points clouds with both high vertical and horizontal densities to support a new generation of urban analysis and applications. As an example, this paper proposes a hierarchical method to automatically extract data points describing road edges, which are then used for reconstructing road edges and identifying accessible passage areas. The proposed approach is a cell-based method consisting of 3 main steps: (1) filtering rough ground points, (2) extracting cells containing data points of the road curb, and (3) eliminating incorrect road curb segments. The method was tested on a pair of 100 m × 100 m tiles of ALS data of Dublin Ireland’s city center with a horizontal point density of about 325 points/m2. Results showed the data points of the road edges to be extracted properly for locations appearing as the road edges with the average distance errors of 0.07 m and the ratio between the extracted road edges and the ground truth by 73.2%.