Cultural heritage plays an important social role in preserving collective identity and history, acting as a link between past, present and future. In this same context, the contribution of technological innovations plays a fundamental role as it provides the tools and solutions needed to address the issues of cultural heritage preservation and enhancement. This study presents a comprehensive review of the application of machine learning (ML) techniques in the field of cultural heritage (CH) protection, highlighting impor-tant developments and innovations in recent years. The main applications of ML and AI methodologies are analyzed, including artefact analysis, restoration, conservation strategies, and enhancing the visitor experience. The available studies are classified according to the areas of application, the types of data and technologies employed and the types of cultural heritage assets they focus on. The classification also highlights potential research challenges and provides indications for future directions. The study shows the increasing adoption of the multidisciplinary approach combining ML and AI with traditional tools of protection and conservation. The discussion is articulated through the reinterpretation of several case studies that demonstrate the real implications of such technologies, including the preventive maintenance of buildings, as well as the digitalization and three-dimensional recreation of artefacts and visitor expe-riences through virtual and augmented reality. This highlights the need for closer collaboration between technicians, conservators, and cultural workers to ensure thoughtful, ethical, and effective integration of these technologies into cultural heritage conservation. (c) 2025 The Author(s). Published by Elsevier Masson SAS. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )

New AI challenges for cultural heritage protection: A general overview

Colace F.
;
Gaeta R.;Lorusso A.;Pellegrino M.;Santaniello D.
2025

Abstract

Cultural heritage plays an important social role in preserving collective identity and history, acting as a link between past, present and future. In this same context, the contribution of technological innovations plays a fundamental role as it provides the tools and solutions needed to address the issues of cultural heritage preservation and enhancement. This study presents a comprehensive review of the application of machine learning (ML) techniques in the field of cultural heritage (CH) protection, highlighting impor-tant developments and innovations in recent years. The main applications of ML and AI methodologies are analyzed, including artefact analysis, restoration, conservation strategies, and enhancing the visitor experience. The available studies are classified according to the areas of application, the types of data and technologies employed and the types of cultural heritage assets they focus on. The classification also highlights potential research challenges and provides indications for future directions. The study shows the increasing adoption of the multidisciplinary approach combining ML and AI with traditional tools of protection and conservation. The discussion is articulated through the reinterpretation of several case studies that demonstrate the real implications of such technologies, including the preventive maintenance of buildings, as well as the digitalization and three-dimensional recreation of artefacts and visitor expe-riences through virtual and augmented reality. This highlights the need for closer collaboration between technicians, conservators, and cultural workers to ensure thoughtful, ethical, and effective integration of these technologies into cultural heritage conservation. (c) 2025 The Author(s). Published by Elsevier Masson SAS. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
2025
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4915275
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