Volume IV-4 | Copyright
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4, 33-40, 2018
https://doi.org/10.5194/isprs-annals-IV-4-33-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  19 Sep 2018

19 Sep 2018

ROOM SHAPES AND FUNCTIONAL USES PREDICTED FROM SPARSE DATA

Y. Dehbi1, N. Gojayeva1, A. Pickert1, J.-H. Haunert1, and L. Plümer1,2 Y. Dehbi et al.
  • 1Institute of Geodesy and Geoinformation, University of Bonn, Meckenheimer Allee 172, Bonn, Germany
  • 2Southwest Jiaotong University, Chengdu, China

Keywords: Indoor Model, CityGML, BIM, Bayesian Classification, Stochastic Reasoning

Abstract. Many researchers used expensive 3D laser scanning techniques to derive indoor models. Few papers describe the derivation of indoor models based on sparse data such as footprints. They assume that floorplans and rooms are rather rectangular and that information on functional use is given. This paper addresses the automatic learning of a classifier which predicts the functional use of housing rooms. The classification is based on features which are widely available such as room areas and orientation. These features are extracted from an extensive database of annotated rooms. A Bayesian classifier is applied which delivers probabilities of competing class hypotheses. In a second step, functional uses are used to predict the shape of the rooms in a further classification.

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