Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 347-354, 2016
https://doi.org/10.5194/isprs-archives-XLI-B5-347-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
15 Jun 2016
ANALYSIS, THEMATIC MAPS AND DATA MINING FROM POINT CLOUD TO ONTOLOGY FOR SOFTWARE DEVELOPMENT
R. Nespeca1 and L. De Luca2 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.
Conference paper (PDF, 2276 KB)


Citation: Nespeca, R. and De Luca, L.: ANALYSIS, THEMATIC MAPS AND DATA MINING FROM POINT CLOUD TO ONTOLOGY FOR SOFTWARE DEVELOPMENT, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B5, 347-354, https://doi.org/10.5194/isprs-archives-XLI-B5-347-2016, 2016.

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