The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 473–478, 2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 473–478, 2020

  12 Aug 2020

12 Aug 2020


G.-A. Nys, R. Billen, and F. Poux G.-A. Nys et al.
  • Geomatics Unit, University of Liège (ULiège), Allée du six Août, 19, 4000 Liège, Belgium

Keywords: LiDAR, Smart Cities, 3D City Model, CityJSON, Point Cloud

Abstract. Point clouds generated from aerial LiDAR and photogrammetric techniques are great ways to obtain valuable spatial insights over large scale. However, their nature hinders the direct extraction and sharing of underlying information. The generation of consistent large-scale 3D city models from this real-world data is a major challenge. Specifically, the integration in workflows usable by decision-making scenarios demands that the data is structured, rich and exchangeable. CityGML permits new advances in terms of interoperable endeavour to use city models in a collaborative way. Efforts have led to render good-looking digital twins of cities but few of them take into account their potential use in finite elements simulations (wind, floods, heat radiation model, etc.). In this paper, we target the automatic reconstruction of consistent 3D city buildings highlighting closed solids, coherent surface junctions, perfect snapping of vertices, etc. It specifically investigates the topological and geometrical consistency of generated models from aerial LiDAR point cloud, formatted following the CityJSON specifications. These models are then usable to store relevant information and provides geometries usable within complex computations such as computational fluid dynamics, free of local inconsistencies (e.g. holes and unclosed solids).