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    • Evolution and Emerging Frontiers of Artificial Intelligence in Cultural Heritage Preservation: A 20-Year Bibliometric Review (2005-2024)

    Review Article (Published On: 19-Jun-2026 )
    DOI : https://doi.org/10.54364/AAIML.2026.63316

    Adewale Segun Alabi, Folasade Jokotade Odekunle, Oluwadamilola Ajoke Alabi, Babatunde Tolulope Adedeji, Kazeem Aderemi Bello, Rendani Wilson Maladzhi, Ilesanmi Daniyan and Adefemi Adeodu

    Adv. Artif. Intell. Mach. Learn., 6 (3):5725-5745

    1. Adewale Segun Alabi: Department of Architecture, Bells University of Technology Ota, Ogun State, Nigeria

    2. Folasade Jokotade Odekunle: Department of Architecture, Bells University of Technology Ota, Ogun State, Nigeria

    3. Oluwadamilola Ajoke Alabi: Department of Architecture, Bells University of Technology Ota, Ogun State, Nigeria

    4. Babatunde Tolulope Adedeji: Institute of Systems Science, Durban University of Technology, Durban South Africa

    5. Kazeem Aderemi Bello: Department of Mechanical Engineering, Durban University of Technology, Durban, South Africa

    6. Rendani Wilson Maladzhi: Department of Mechanical Engineering, Durban University of Technology, Durban, South Africa

    7. Ilesanmi Daniyan: Department of Mechatronics Engineering, Centre for Artificial Intelligence Bells University of Technology, P. M. B. 1015 Ota, Ogun State, Nigeria.

    8. Adefemi Adeodu: Department of Project Management Centre for Artificial Intelligence Bells University of Technology, P. M. B. 1015 Ota, Ogun State, Nigeria.

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    DOI: 10.54364/AAIML.2026.63316

    Article History: Received on: 14-Nov-25, Accepted on: 12-Jun-26, Published on: 19-Jun-26

    Corresponding Author: Adewale Segun Alabi

    Email: adewalesegunalabi12@gmail.com

    Citation: Adewale Segun Alabi, et al. Evolution and Emerging Frontiers of Artificial Intelligence in Cultural Heritage Preservation: A 20-Year Bibliometric Review (2005-2024). Advances in Artificial Intelligence and Machine Learning.2026 (Ahead of Print) https://dx.doi.org/10.54364/AAIML.2026.63316


    Abstract

    This study conducts a comprehensive bibliometric review mapping the intellectual structure and evolution of Artificial Intelligence (AI) in cultural heritage preservation (2005–2024). Analysing 341 Scopus/Web of Science publications using VOSviewer, this study employs performance analysis and science mapping (co-authorship, co-citation, and keyword co-occurrence) in accordance with PRISMA guidelines. Three distinct growth phases are identified: emergence (2005–2010), consolidation (2011–2017), and acceleration (2018–2024). Foundational works bridged ecological modelling and immersive technologies with modern AI. Thematic analysis reveals dominant clusters: computer vision/3D reconstruction, deep learning/sensor fusion, and hyperspectral material analysis. Emerging keywords ("physics-informed machine learning," "transfer learning") signal shifts towards domain-specific AI integration and cross-site generalization. The review quantifies the field's evolution, collaboration networks, and conceptual shifts, highlighting the interdisciplinary transfer of methodologies (e.g., from ecology). It provides data-driven recommendations for future research, including scalable AI frameworks and ethical guidelines. These contributions advance Sustainable Development Goals (SDGs), particularly SDG 11.4 (safeguarding heritage) and SDG 4.7 (heritage education).

     

     


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