Volume XLII-4/W18
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 541–546, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-541-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 541–546, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-541-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  18 Oct 2019

18 Oct 2019

AUTOMATIC BUILDING EXTRACTION FROM LIDAR POINT CLOUD DATA IN THE FUSION OF ORTHOIMAGE

B. Hujebri1, M. Ebrahimikia2, and H. Enayati2 B. Hujebri et al.
  • 1School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, North Kargar Ave., Jalal Al. Ahmad Crossing, Tehran, Iran
  • 2K.N.Toosi University of Technology, Mirdamad, Tehran, Iran

Keywords: Building, Segmentation, Mean Shift, Image, LiDAR, Point Cloud

Abstract. Three-dimensional building models are important in various applications such as disaster management and urban planning. In this paper, a method based on the fusion of LiDAR point cloud and aerial image data sources has been proposed. The first step of the proposed method is to separate ground and non-ground (that contain 3d objects like buildings, trees, …) points using cloth simulation filtering and then normalize the non-ground points. This research experiment applied a 0.1 threshold for the z component of the normal vector to remove wall points, and 2-meter height threshold to remove off-terrain objects lower than the minimum building height. It is possible to discriminate vegetation and building based on spectral information from orthoimage. After elimination of vegetation points, the mean shift algorithm applied on remaining points to detect buildings. This method provides good performance in dense urban areas with complex ground covering such as trees, shrubs, short walls, and vehicles.