Volume XLII-1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 127-132, 2018
https://doi.org/10.5194/isprs-archives-XLII-1-127-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 127-132, 2018
https://doi.org/10.5194/isprs-archives-XLII-1-127-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  26 Sep 2018

26 Sep 2018

BUILDING BOUNDARY EXTRACTION FROM LIDAR DATA USING A LOCAL ESTIMATED PARAMETER FOR ALPHA SHAPE ALGORITHM

R. C. dos Santos1, M. Galo2, and A. C. Carrilho1 R. C. dos Santos et al.
  • 1São Paulo State University - UNESP, Graduate Program in Cartographic Sciences, Presidente Prudente, São Paulo, Brazil
  • 2São Paulo State University - UNESP, Dept. of Cartography, Presidente Prudente, São Paulo, Brazil

Keywords: Building Boundary Extraction, LiDAR Data, Alpha Shape Algorithm, Delaunay Triangulation, Average Point Spacing, Point density

Abstract. The α-shape algorithm is a very common option to extract building boundaries from LiDAR data. This algorithm is normally executed in 2D space considering a parameter α as a binary classifier which controls the distinctiveness of points whether or not they belong to the object boundary. For point cloud data, this parameter is directly related to the local point density and the level of detail of building boundaries. Studies that have explored this concept usually consider a unique parameter α to extract all buildings in the dataset. However, the point density can have a considerable variation along the point cloud and, in this case, the use a global parameter may not be the best choice. Alternatively, this paper proposes a data-driven method that estimates a local parameter for each building. The method evaluation considered six test areas with different levels of complexity, selected from a LiDAR dataset acquired over the city of Presidente Prudente/Brazil. From the qualitative and quantitative analysis, it could be seen that the proposed method generated better results than when a global parameter is used. The proposed method was also able to withstand density variation among the LiDAR data, having a positional accuracy around 0.22 m, against 0.40 m of global parameter.