Volume XL-7/W2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W2, 161-166, 2013
https://doi.org/10.5194/isprsarchives-XL-7-W2-161-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W2, 161-166, 2013
https://doi.org/10.5194/isprsarchives-XL-7-W2-161-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

  29 Oct 2013

29 Oct 2013

Quality evaluation of 3D city building Models with automatic error diagnosis

J.-C. Michelin1,2, J. Tierny2, F. Tupin2, C. Mallet1, and N. Paparoditis1 J.-C. Michelin et al.
  • 1IGN/SR, MATIS, Université Paris Est, 73 avenue de Paris, 94160 Saint-Mande, France
  • 2Institut Mines-Télécom, Télécom ParisTech, LTCI, 46 rue Barrault, 75634 Paris Cedex 13, France

Keywords: 3D City Models, buildings, error evaluation, self-diagnosis, feature extraction, classification

Abstract. Automatic building modelling allows a cost effective access to 3D semantic information of cities. However, even state-of-the-art algorithms have intrinsic limits and many errors exist in 3D reconstructions, requiring expensive manual corrections. A new approach is proposed in this paper for the automatic diagnosis of 3D building databases in urban areas. A novel error taxonomy which allows a subsequent high-level diagnosis is first proposed. Then, relevant raster and vector features are extracted from very high resolution multi-view images and Digital Surface Models so as that to retrieve such errors. In a supervised way, a set of functions is presented in order to take high-level decisions from these low-level features. Experiments on 355 buildings in an European dense city center with 10 cm airborne images demonstrate the high accuracy on error detection and show promising results.