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Digitalization of Cultural Heritage with Artificial Intelligence: Machine Learning and Deep Learning Solutions

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 March 2026 | Viewed by 1751

Special Issue Editors

Dr. Marcello La Guardia

E-Mail Website
Guest Editor
Department of Engineering, University of Messina, Sant’Agata, 98158 Messina, Italy
Interests: geomatics; GIS analysis; digital twins; UAV acquisition; digital photogrammetry; 3D modelling; cultural heritage; machine learning; virtual reality; augmented reality; remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Valentina Bonora

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Università degli Studi di Firenze, Florence, Italy
Interests: geomatics; built heritage; indoor and outdoor mobile mapping systems; high-resolution 3D models; segmentation; classification; reproductions of sculptural works; digital archives; reuse and sharing of 3D data; technologies applied to educational projects
Special Issues, Collections and Topics in MDPI journals
Dr. Andrea Masiero

E-Mail Website
Guest Editor
Secretary of ISPRS WG I/7-Mobile Mapping Technology, Interdepartmental Research Center of Geomatics (CIRGEO), University of Padua, Padua, Italy
Interests: geomatics; mobile mapping; laser scanning; photogrammetry; remote sensing; navigation; data processing; machine learning; unmanned aerial vehicles; cultural heritage
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to submit original contributions on the topic of the Digitalization of CH (cultural heritage) with AI (artificial intelligence), with a particular reference to ML (machine learning) and DL (deep learning) solutions.

The Digitalization of CH represents a fundamental process for the conservation and fruition of cultural goods. Recent technological advances in Geomatics have allowed experts to improve the quality of the digitalization process on the basis of innovative survey technologies. At the same time, the recent development of ML and DL applications has allowed us to make a revolution in the field of segmentation and classification processes in bidimensional and tridimensional domains. This great effort offered by AI represents an innovative possibility for improvements in the CH digitalization process, opening new perspectives for valorization purposes.

This Special issue aims to collect high-quality papers dealing with new challenges related to the application and the integration of ML, DL, and Geomatics for innovative cultural heritage digitalization processes aimed at conservation and fruition purposes.

We look forward to receiving your contributions.

Dr. Marcello La Guardia
Dr. Valentina Bonora
Dr. Andrea Masiero
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cultural heritage
  • TLS
  • photogrammetry
  • machine learning
  • deep learning

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Published Papers (2 papers)

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Research

34 pages, 1982 KB  
Article
Knowledge Graphs and Artificial Intelligence for the Implementation of Cognitive Heritage Digital Twins
by Achille Felicetti, Aida Himmiche and Miriana Somenzi
Appl. Sci. 2025, 15(18), 10061; https://doi.org/10.3390/app151810061 - 15 Sep 2025
Viewed by 512
Abstract
This paper explores the integration of Artificial Intelligence and semantic technologies to support the creation of intelligent Heritage Digital Twins, digital constructs capable of representing, interpreting, and reasoning over cultural data. This study focuses on transforming the often fragmented and unstructured documentation produced [...] Read more.
This paper explores the integration of Artificial Intelligence and semantic technologies to support the creation of intelligent Heritage Digital Twins, digital constructs capable of representing, interpreting, and reasoning over cultural data. This study focuses on transforming the often fragmented and unstructured documentation produced in cultural heritage into coherent Knowledge Graphs aligned with internationally recognised standards and ontologies. Two complementary AI-assisted workflows are proposed: one for extracting and formalising structured knowledge from heritage science reports and another for enhancing AI models through the integration of curated ontological knowledge. The experiments demonstrate how this synergy facilitates both the retrieval and the reuse of complex information while ensuring interpretability and semantic consistency. Beyond technical efficacy, this paper also addresses the ethical implications of AI use in cultural heritage, with particular attention to transparency, bias mitigation, and meaningful representation of diverse narratives. The results highlight the importance of a reflexive and ethically grounded deployment of AI, where knowledge extraction and machine learning are guided by structured ontologies and human oversight, to ensure conceptual rigour and respect for cultural complexity. Full article
(This article belongs to the Special Issue Digitalization of Cultural Heritage with Artificial Intelligence: Machine Learning and Deep Learning Solutions)
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31 pages, 12749 KB  
Article
Research on Digital Restoration and Innovative Utilization of Taohuawu Woodblock New Year Prints Based on Edge Detection and Color Clustering
by Yingluo Dai, Fei Ju and Yuhang Wen
Appl. Sci. 2025, 15(16), 9081; https://doi.org/10.3390/app15169081 - 18 Aug 2025
Viewed by 467
Abstract
Taohuawu woodblock New Year prints are one of the most representative traditional multicolor woodblock print forms from the Jiangnan region of China and are recognized as an Intangible Cultural Heritage at the provincial level in Jiangsu. However, the development of mechanized and high-tech [...] Read more.
Taohuawu woodblock New Year prints are one of the most representative traditional multicolor woodblock print forms from the Jiangnan region of China and are recognized as an Intangible Cultural Heritage at the provincial level in Jiangsu. However, the development of mechanized and high-tech production methods, combined with the declining role of traditional festive customs in modern society, has posed significant challenges to the preservation and transmission of this art form. Existing digital preservation efforts mainly focus on two-dimensional scanning and archival storage, largely neglecting the essential processes of color separation and multicolor overprinting. In this study, a digital restoration method is proposed that integrates image processing, color clustering, and edge detection techniques for the efficient reconstruction of the traditional multicolor woodblock overprinting process. The approach applies the K-means++ clustering algorithm to extract the dominant colors and reconstruct individual color layers, in combination with CIELAB color space transformation to enhance color difference perception and improve segmentation accuracy. To address the uncertainty in determining the number of color layers, the elbow method, silhouette coefficient, and Calinski-Harabasz index are employed as clustering evaluation methods to identify the optimal number of clusters. The proposed approach enables the generation of complete, standardized digital color separations, providing a practical pathway for efficient reproduction and intelligent application of TWNY Prints, contributing to the digital preservation and innovative revitalization of intangible cultural heritage. Full article
(This article belongs to the Special Issue Digitalization of Cultural Heritage with Artificial Intelligence: Machine Learning and Deep Learning Solutions)
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