The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLI-B5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 347–354, 2016
https://doi.org/10.5194/isprs-archives-XLI-B5-347-2016
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 347–354, 2016
https://doi.org/10.5194/isprs-archives-XLI-B5-347-2016

  15 Jun 2016

15 Jun 2016

ANALYSIS, THEMATIC MAPS AND DATA MINING FROM POINT CLOUD TO ONTOLOGY FOR SOFTWARE DEVELOPMENT

R. Nespeca1 and L. De Luca2 R. Nespeca and L. De Luca
  • 1DICEA, Dept. of Civil, Building Engineering and Architecture, Engineering Faculty, Polytechnic University of Marche, Ancona, Italy
  • 2MAP (UMR 3495 CNRS/MCC) - Modèles et simulations pour l’Architecture et le Patrimoine, CNRS, Marseille, France

Keywords: Analysis, Thematic Maps, Point Cloud, Cultural Heritage, Degradation Recognition, Restoration

Abstract. The primary purpose of the survey for the restoration of Cultural Heritage is the interpretation of the state of building preservation. For this, the advantages of the remote sensing systems that generate dense point cloud (range-based or image-based) are not limited only to the acquired data. The paper shows that it is possible to extrapolate very useful information in diagnostics using spatial annotation, with the use of algorithms already implemented in open-source software. Generally, the drawing of degradation maps is the result of manual work, so dependent on the subjectivity of the operator. This paper describes a method of extraction and visualization of information, obtained by mathematical procedures, quantitative, repeatable and verifiable. The case study is a part of the east facade of the Eglise collégiale Saint-Maurice also called Notre Dame des Grâces, in Caromb, in southern France. The work was conducted on the matrix of information contained in the point cloud asci format. The first result is the extrapolation of new geometric descriptors. First, we create the digital maps with the calculated quantities. Subsequently, we have moved to semi-quantitative analyses that transform new data into useful information. We have written the algorithms for accurate selection, for the segmentation of point cloud, for automatic calculation of the real surface and the volume. Furthermore, we have created the graph of spatial distribution of the descriptors. This work shows that if we work during the data processing we can transform the point cloud into an enriched database: the use, the management and the data mining is easy, fast and effective for everyone involved in the restoration process.