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
Quality evaluation of 3D city building Models with automatic error diagnosis
J.-C. Michelin1,2, J. Tierny2, F. Tupin2, C. Mallet1, and N. Paparoditis1 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.
Conference paper (PDF, 5428 KB)


Citation: Michelin, J.-C., Tierny, J., Tupin, F., Mallet, C., and Paparoditis, N.: Quality evaluation of 3D city building Models with automatic error diagnosis, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W2, 161-166, https://doi.org/10.5194/isprsarchives-XL-7-W2-161-2013, 2013.

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