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, 847–854, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-847-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 847–854, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-847-2021

  28 Jun 2021

28 Jun 2021

DECAY CLASSIFICATION USING ARTIFICIAL INTELLIGENCE

E. C. Giovannini1, A. Tomalini1, E. Pristeri2, L. Bergamasco2, and M. Lo Turco1 E. C. Giovannini et al.
  • 1DAD, Dept. of Architecture and Design, Politecnico di Torino, Turin, Italy
  • 2LINKS Foundation, Leading Innovation & Knowledge for Society, Turin, Italy

Keywords: Machine Learning, decay map, H-BIM, data analysis, pattern recognition

Abstract. The paper presents DECAI - DEcay Classification using Artificial Intelligence, a novel study using machine learning algorithms to identify materials, degradations or surface gaps of an architectural artefact in a semi-automatic way. A customised software has been developed to allow the operator to choose which categories of materials to classify, and selecting sample data from an orthophoto of the artefact to train the machine learning algorithms. Thanks to Visual Programming Language algorithms, the classification results are directly imported into the H-BIM environment and used to enrich the H-BIM model of the artefact. To date, the developed tool is dedicated to research use only; future developments will improve the graphical interface to make this tool accessible to a wider public.