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Digital Heritage Classification via Machine Learning and H-BIM

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Abstract

This research presents the results of a Ph.D. thesis, discussed in June 2022 and involving Italian and French research institutes, on the topic of semantic annotation transfer and retrieval for architectural heritage. The developed methodological approach combines statistical methods based on Artificial Intelligence with H-BIM systems and collaborative reality-based annotation platforms.

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Data availability

The data that support the findings of this study are available from the author, upon reasonable request.

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Acknowledgements

The author would like to thank her Ph.D. supervisors Marco Giorgio Bevilacqua (University of Pisa), Gabriella Caroti (University of Pisa), Livio De Luca (UMR MAP CNRS/MC Marseille), Andrea Piemonte (University of Pisa) and Philippe Véron (ENSAM Aix-en-Provence).

Funding

Università Italo Francese, VINCI 2019—Chapter II, Valeria Croce, Regione Toscana, Borse di dottorato POR FSE Toscana 2014/2020, Valeria Croce

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Authors and Affiliations

  1. Department of Energy Engineering, Systems, Land and Construction Engineering, University of Pisa, Pisa, Italy

    Valeria Croce

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  1. Valeria Croce
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Correspondence to Valeria Croce.

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The author declares no conflict of interest.

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Croce, V. Digital Heritage Classification via Machine Learning and H-BIM. Nexus Netw J 25 (Suppl 1), 415–421 (2023). https://doi.org/10.1007/s00004-023-00725-0

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  • DOI: https://doi.org/10.1007/s00004-023-00725-0

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