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Automatic building modelling allows a cost effective access to 3D semantic information of cities. However, even state-of-the-artalgorithms have intrinsic limits and many errors exist in 3D reconstructions, requiring expensive manual corrections. A new approachis proposed in this paper for the automatic diagnosis of 3D building databases in urban areas. A novel error taxonomy which allowsa subsequent high-level diagnosis is first proposed. Then, relevant raster and vector features are extracted from very high resolutionmulti-view images and Digital Surface Models so as that to retrieve such errors. In a supervised way, a set of functions is presented inorder to take high-level decisions from these low-level features. Experiments on 355 buildings in an European dense city center with10 cm airborne images demonstrate the high accuracy on error detection and show promising results

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

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