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Machine Learning for the Semi-Automatic 3D Decay Segmentation and Mapping of Heritage Assets

Author(s): ORCID (DICATECh - Department of Civil, Environmental, Land, Construction and Chemistry, Polytechnic University of Bari, Bari, Italy)
ORCID (DICATECh - Department of Civil, Environmental, Land, Construction and Chemistry, Polytechnic University of Bari, Bari, Italy)
ORCID (DICATECh - Department of Civil, Environmental, Land, Construction and Chemistry, Polytechnic University of Bari, Bari, Italy)
ORCID (DICATECh - Department of Civil, Environmental, Land, Construction and Chemistry, Polytechnic University of Bari, Bari, Italy)
Medium: journal article
Language(s): English
Published in: International Journal of Architectural Heritage
Page(s): 1-19
DOI: 10.1080/15583058.2023.2287152
Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1080/15583058.2023.2287152.
  • About this
    data sheet
  • Reference-ID
    10750848
  • Published on:
    14/01/2024
  • Last updated on:
    14/01/2024
 
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