Futurescape of Heritage Preservation: Integrating AI, Digital Twins, and Multi-Scale Technologies for Cultural Sustainability

A special issue of Heritage (ISSN 2571-9408).

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2174

Special Issue Editor

Special Issue Information

Dear Colleagues,

In the face of increasing environmental challenges and the urgent need to preserve cultural heritage, this special issue aims to highlight pioneering research and technological advancements in the domain of preventive preservation, digital twins, and AI-driven methodologies for cultural heritage preservation. It brings into focus the synergistic application of multi-modal and multi-scale digitization techniques, AI, and digital twin technologies to create resilient and sustainable frameworks for managing heritage assets, particularly those in remote locations.

This special issue will explore a comprehensive range of topics, including, but not limited to:

  • The development and implementation of digital twin models for built heritage, emphasizing support for multi-scale and multi-modal data integration.
  • Advanced digitization strategies tailored to enhance digital twins of cultural heritage sites, facilitating detailed and accurate representations.
  • Innovations in portable, non-destructive measurement systems utilizing miniaturized sensors for physical and chemical monitoring of heritage assets from both ground and aerial perspectives.
  • AI-enabled methodologies for identifying and reverse-engineering environmental and anthropogenic threat factors to cultural heritage, alongside assessing their impacts.
  • The fusion of AI-powered multimodal data methods to integrate diverse data sources such as remote sensing climate data, regional natural disaster statistics, governmental records, and on-site measurements for a holistic view of heritage sites.
  • Trustworthy AI-driven decision support systems for the preventive preservation of built heritage, aimed at predicting and mitigating potential damages before they occur.

Contributions to this special issue will target a multidisciplinary audience, ranging from researchers and academics interested in the technical data and methodologies, to stakeholders, heritage managers, and practitioners looking for practical applications and decision support tools. Additionally, authorities and the general public will find value in real-time monitoring solutions, long-term analysis, and the facilitation of community engagement through crowdsourcing and citizen science initiatives.

This special issue focuses on innovative approaches to advance the scientific and technological landscape of cultural heritage preservation and to promote sustainable practices and strategies for climate change mitigation and the safeguarding of global cultural heritage for future generations.

Dr. George Pavlidis
Guest Editor

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. Heritage is an international peer-reviewed open access monthly 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 1600 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

  • preventive preservation
  • cultural heritage digital twins
  • artificial intelligence in heritage conservation
  • multi-scale digitization
  • multi-modal data integration
  • non-destructive measurement techniques
  • sensor technologies for heritage sites
  • AI-driven decision support systems
  • climate change mitigation for cultural heritage
  • remote heritage asset management

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Published Papers (1 paper)

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Research

22 pages, 10489 KB  
Article
From Contemporary Datasets to Cultural Heritage Performance: Explainability and Energy Profiling of Visual Models Towards Textile Identification
by Evangelos Nerantzis, Lamprini Malletzidou, Eleni Kyratzopoulou, Nestor C. Tsirliganis and Nikolaos A. Kazakis
Heritage 2025, 8(11), 447; https://doi.org/10.3390/heritage8110447 (registering DOI) - 24 Oct 2025
Viewed by 149
Abstract
The identification and classification of textiles play a crucial role in archaeometric studies, in the vicinity of their technological, economic, and cultural significance. Traditional textile analysis is closely related to optical microscopy and observation, while other microscopic, analytical, and spectroscopic techniques prevail over [...] Read more.
The identification and classification of textiles play a crucial role in archaeometric studies, in the vicinity of their technological, economic, and cultural significance. Traditional textile analysis is closely related to optical microscopy and observation, while other microscopic, analytical, and spectroscopic techniques prevail over fiber identification for composition purposes. This protocol can be invasive and destructive for the artifacts under study, time-consuming, and it often relies on personal expertise. In this preliminary study, an alternative, macroscopic approach is proposed, based on texture and surface textile characteristics, using low-magnification images and deep learning models. Under this scope, a publicly available, imbalanced textile image dataset was used to pretrain and evaluate six computer vision architectures (ResNet50, EfficientNetV2, ViT, ConvNeXt, Swin Transformer, and MaxViT). In addition to accuracy, energy efficiency and ecological footprint of the process were assessed using the CodeCarbon tool. The results indicate that two of the convolutional neural network models, Swin and EfficientNetV2, both deliver competitive accuracies together with low carbon emissions, in comparison to the transformer and hybrid models. This alternative, promising, sustainable, and non-invasive approach for textile classification demonstrates the feasibility of developing a custom, heritage-based image dataset. Full article
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