International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-2/W17
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W17, 173–177, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W17-173-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W17, 173–177, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W17-173-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  29 Nov 2019

29 Nov 2019

BUILDING FACADE AND ROOFTOP SEGMENTATION BY NORMAL ESTIMATION FROM UAV DERIVED RGB POINT CLOUD

S. K. P. Kushwaha1, K. R. Dayal2, A. Singh3, and K. Jain1 S. K. P. Kushwaha et al.
  • 1Geomatics Group, Department of Civil Engineering, Indian Institute of Technology, Roorkee, Uttarakhand, India
  • 2IRSTEA, 361 rue J.F. Breton Montpellier, France
  • 3Forestry and Ecology Department, Indian Institute of Remote Sensing, Dehradun, Uttarakhand, India

Keywords: UAV, Photogrammetry, Point Cloud, Classification, Segmentation, Normal estimation

Abstract. Point cloud segmentation is a significant process to organise an unstructured point cloud. In this study, RGB point cloud was generated with the help of images acquired from an Unmanned Aerial Vehicle (UAV). A dense urban area was considered with varying planar features in the built-up environment along with buildings with different floors. Initially, using Cloth Simulation Filter (CSF) filter, the ground and the non-ground features in the Point Cloud Data (PCD) were segmented, with non-ground features comprising trees and buildings and ground features comprising roads, ground vegetation, and open land. Subsequently, using CANUPO classifier the trees and building points were classified. Noise filtering removed the points which have less density in clusters. Point cloud normals were generated for the building points. For segmentation building elements, normal vector components in different directions (X component, Y component and Z component) were used to segment out the facade, and the roof points of the buildings as the surface normals corresponding to the roof will have a higher contribution in the z component of the normal vector. The validation of the segmentation is done by comparing the results with manually identified roof points and façade points in the point cloud. Overall accuracies obtained for building roof and building facade segmentation are 90.86 % and 84.83 % respectively.