Volume XLII-4/W10 | Copyright
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W10, 179-186, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  12 Sep 2018

12 Sep 2018


R. Sulzer1, P. Nourian1, M. Palmieri2, and J. C. van Gemert1 R. Sulzer et al.
  • 1Delft University of Technology, the Netherlands
  • 2Arup Amsterdam, the Netherlands

Keywords: Seismic Building Structural Type, Classification, Machine Learning, Point Cloud, Shape Descriptor, Shape-DNA

Abstract. This paper investigates automatic prediction of seismic building structural types described by the Global Earthquake Model (GEM) taxonomy, by combining remote sensing, cadastral and inspection data in a supervised machine learning approach. Our focus lies on the extraction of detailed geometric information from a point cloud gained by aerial laser scanning. To describe the geometric shape of a building we apply Shape-DNA, a spectral shape descriptor based on the eigenvalues of the Laplace-Beltrami operator. In a first experiment on synthetically generated building stock we succeed in predicting the roof type of different buildings with accuracies above 80%, only relying on the Shape-DNA. The roof type of a building thereby serves as an example of a relevant feature for predicting GEM attributes, which cannot easily be identified and described by using traditional methods for shape analysis of buildings. Further research is necessary in order to explore the usability of Shape-DNA on real building data. In a second experiment we use real-world data of buildings located in the Groningen region in the Netherlands. Here we can automatically predict six GEM attributes, such as the type of lateral load resisting system, with accuracies above 75% only by taking a buildings footprint area and year of construction into account.

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