Volume XLII-2/W9
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W9, 369-375, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W9-369-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W9, 369-375, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W9-369-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  31 Jan 2019

31 Jan 2019

KNOWLEDGE-BASED FRAMEWORK FOR AUTOMATIC SEMANTISATION AND RECONSTRUCTION OF MILITARY ARCHITECTURE ON CITY-SCALE MODELS

A. Gros1, K. Jacquot1, and T. Messaoudi2 A. Gros et al.
  • 1MAP-ARIA, UMR CNRS-MC n° 3495, ENS d'Architecture de Lyon, France
  • 2MAP-CRAI, UMR CNRS-MC n° 3495, ENS d'Architecture de Nancy, France

Keywords: city-scale model, knowledge-based modelling, architectural heritage, bastioned fortifications, automation in data processing, point cloud processing and analysis

Abstract. The scale models of fortified towns belonging to the Plans-Reliefs collection are exceptional witnesses of the formation of the French territory. The aim of the URBANIA project is the valorisation and the diffusion of this heritage through the creation of virtual models. The town scale model of Strasbourg at 1:600 currently exhibited in the Historical Museum of Strasbourg was selected as a case study. We develop and experiment an automatic procedure to identify and reconstruct military architecture works from point cloud digitisation of this fragile and bulky heritage. A priori knowledge formalized in a domain ontology informs the identification of the works – via geometrical feature comparison and consistency evaluation within the fortification system morphology – and their parametric 3D reconstruction refined by direct fit to the initial point cloud.