International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-4/W16
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W16, 177–184, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W16-177-2019
© 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/W16, 177–184, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W16-177-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  01 Oct 2019

01 Oct 2019

PROBABILITY DENSITY BASED CLASSIFICATION AND RECONSTRUCTION OF ROOF STRUCTURES FROM 3D POINT CLOUDS

Y. Dehbi1, S. Koppers1, and L. Plümer2 Y. Dehbi et al.
  • 1Institute of Geodesy and Geoinformation, University of Bonn, Germany
  • 2Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China

Keywords: city model, probability density function, roof, dormer, machine learning, support vector machine

Abstract. 3D building models including roofs are a key prerequisite in many fields of applications such as the estimation of solar suitability of rooftops. The accurate reconstruction of roofs with dormers is sometimes challenging. Without careful separation of the dormer points from the points on the roof surface, the estimation of the roof areas is distorted in a most characteristic way, which then let the dormer points appear as white noise. The characteristic distortion of the density distribution of the defects by dormers in comparison to the expected normal distribution is the starting point of our method. We propose a hierarchical method which improves roof reconstruction from LiDAR point clouds in a model-based manner separating dormer points from roof points using classification methods. The key idea is to exploit probability density functions (PDFs) to reveal roof properties and design skilful features for a supervised learning method using support vector machines (SVMs). Properties of the PDFs of measures such as residuals of model-based estimated roof models are used among others. A clustering step leads to a semantic segmentation of the point cloud enabling subsequent reconstruction. The approach is tested based on real data as well as simulated point clouds. The latter allow for experiments for various roof and dormer types with different parameters using an implemented simulation toolbox which generates virtual buildings and synthetic point clouds.