The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 329–336, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-329-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 329–336, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-329-2021

  28 Jun 2021

28 Jun 2021

AN EFFICIENT REPRESENTATION OF 3D BUILDINGS: APPLICATION TO THE EVALUATION OF CITY MODELS

O. Ennafii1,2,3, A. Le Bris1, F. Lafarge2, and C. Mallet1 O. Ennafii et al.
  • 1LASTIG, Univ Gustave Eiffel, ENSG, IGN, F-94160 Saint-Mandé, France
  • 2INRIA, TITANE, 06902 Sophia Antipolis, France
  • 3Gambi-M, SARA, 77420 Champs-sur-Marne, France

Keywords: Error taxonomy, 3D building models, Quality evaluation, 3D feature representation, ScatNet, Graph kernels, Classification

Abstract. City modeling consists in building a semantic generalized model of the surface of urban objects. These could be seen as a special case of Boundary representation surfaces. Most modeling methods focus on 3D buildings with Very High Resolution overhead data (images and/or 3D point clouds). The literature abundantly addresses 3D mesh processing but frequently ignores the analysis of such models. This requires an efficient representation of 3D buildings. In particular, for them to be used in supervised learning tasks, such a representation should be scalable and transferable to various environments as only a few reference training instances would be available. In this paper, we propose two solutions that take into account the specificity of 3D urban models. They are based on graph kernels and Scattering Network. They are here evaluated in the challenging framework of quality evaluation of building models. The latter is formulated as a supervised multilabel classification problem, where error labels are predicted at building level. The experiments show for both feature extraction strategy strong and complementary results (F-score > 74% for most labels). Transferability of the classification is also examined in order to assess the scalability of the evaluation process yielding very encouraging scores (F-score > 86% for most labels).