AI-Driven Sustainability in Heritage Science
A special issue of Sustainability (ISSN 2071-1050).
Deadline for manuscript submissions: 30 April 2026 | Viewed by 192
Special Issue Editors
Interests: archaeometry; cultural heritage conservation; geomaterials
Special Issues, Collections and Topics in MDPI journals
Interests: archaeometry; cultural heritage conservation; pigments; provenance; spectroscopic techniques
Special Issues, Collections and Topics in MDPI journals
Interests: dyes and pigments; organic binders; spectroscopic techniques; cultural heritage materials; gels
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We are pleased to invite you to contribute to the Special Issue of the journal Sustainability dedicated to ‘AI-Driven Sustainability in Heritage Science’.
Artificial intelligence (AI) can significantly enhance the sustainability of heritage science research by optimizing data analysis, preservation techniques, and resource management, fostering multidisciplinary collaborations between high-tech sectors and traditional disciplines like humanities. AI-powered algorithms can process vast amounts of historical and environmental data, identifying patterns that help predict degradation risks and inform conservation strategies. AI-driven data fusion can integrate information from diverse analytical methods and historical records to create comprehensive models of heritage sites and artifacts. This holistic approach improves accuracy in monitoring degradation, predicting risks, and developing targeted conservation strategies. Machine learning models can also assist in non-invasive restoration methods, reducing the need for physical interventions that may cause harm to the environment or the operator. Additionally, AI-driven automation can streamline documentation and digitization efforts, ensuring long-term accessibility to cultural heritage materials while minimizing resource consumption. By integrating AI, heritage science can achieve more efficient, cost-effective, and sustainable preservation of historical artifacts and sites. This Special Issue aims to collect scientific contributions on the latest advancements that bring AI and heritage science together for research with higher sustainability.
Topics of interest for this Special Issue include, but are not limited to the following:
- AI-driven data processing and data fusion
- Sustainable AI-driven conservation
- Machine learning models for sustainable preservation
- AI-driven automated documentation and digitization
- Machine learning models for archaeometric studies
Dr. Laura Medeghini
Dr. Michela Botticelli
Dr. Alessandro Ciccola
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. Sustainability 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
- heritage science
- artificial intelligence
- sustainable conservation
- AI-driven data processing
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