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