Volume XLII-2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1199-1205, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-1199-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1199-1205, 2018
https://doi.org/10.5194/isprs-archives-XLII-2-1199-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 May 2018

30 May 2018

EXTRACTION OF BUILDING ROOF EDGES FROM LIDAR DATA TO OPTIMIZE THE DIGITAL SURFACE MODEL FOR TRUE ORTHOPHOTO GENERATION

E. Widyaningrum1,2, R. C. Lindenbergh1, B. G. H. Gorte3, and K. Zhou1 E. Widyaningrum et al.
  • 1Dept. of Geoscience and Remote Sensing, Delft University of Technology, The Netherlands
  • 2Centre for Topographic Base Mapping and Toponyms, Geospatial Information Agency, Indonesia
  • 3University of New South Wales, Australia

Keywords: LiDAR, Aerial photos, Edge Detection, DSM, True Orthophoto

Abstract. Various kinds of urban applications require true orthophotos. True orthophoto generation requires a DSM (Digital Surface Model) to project the photo orthogonally and minimize geometric distortion due to topographic variance. DSMs are often generated from airborne laser scan data. In urban scenes, DSM data may fail to deliver sharp and straight building roof edges. This will affect the quality of the resulting orthophotos. Therefore, it is necessary to incorporate good quality building outlines as breaklines during DSM interpolation. This study proposes a data-driven approach to construct building roof outlines from LiDAR point clouds by a workflow consisting of the following steps: given roof segments, roof boundary points are extracted using a concave hull algorithm. Straight edges may be difficult to find in complex roof configurations. Therefore, two ingredients are combined. First, RanSAC corner point preselection, and second, DBSCAN-based clustering of edge points. The method is demonstrated on an area of ±1.2 km2 containing 42 buildings of different characteristics. A quality assessment shows that the proposed method is able to deliver 92 % of building lines with acceptable geometric accuracy in comparison to a building line in the base map.