uk

Article

Integration of artificial intelligence into the restoration of architectural monuments: Methods and prospects

Oleh Nester
Abstract

The study aimed to identify the potential of intelligent digital technologies in assessing the condition of architectural heritage sites and justifying necessary interventions. Methods of systematisation, comparative and comparative-legal analysis, and case analysis were employed. The study established that artificial intelligence in the field of architectural heritage conservation improved professional expertise, digital documentation, condition analysis, forecasting and support for restoration decisions. The effectiveness was determined by data quality, expert validation, system compatibility and algorithm transparency. At the same time, key challenges remained, including incomplete automation, the complexity of integrating digital environments, and the need for human oversight. The global regulatory and ethical framework stipulated that the application of artificial intelligence in restoration must be based on the principles of authenticity, human oversight, accountability, and the use of reliable data. The study determined that Ukrainian legislation does not yet contain specific legal regulations on the use of artificial intelligence in restoration; therefore, its application was primarily linked to documentation, standardisation of damage assessment, data preparation and the professional training of specialists. The case studies of the Dunhuang Mogao Grottoes, Mezgit Castle, St Peter’s Basilica, and Lausanne Cathedral demonstrated established approaches to the use of artificial intelligence in restoration practice, as well as in the monitoring and forecasting of the condition of heritage sites. Ukrainian practices in Lviv (Historic Centre Ensemble), Odesa (Odesa Historic Centre) and Chernihiv (T. Shevchenko Chernihiv Regional Academic Music and Drama Theatre) represented a predominantly artificial intelligence-ready environment for the future implementation of such solutions. Therefore, the opportunities for the application of artificial intelligence were linked to the transition to comprehensive integrated systems for analysis, modelling and decision support, provided that the principles of authenticity, minimal intervention, scientific verification and mandatory human oversight were upheld. The practical significance of the possible use of results by restorers, architects, engineers and cultural heritage authorities in restoration practice, during the documentation and assessment of the condition of monuments, and in planning measures for their preservation

Keywords

heritage; digital recording; damage; digital replicas; condition monitoring

Download article

Received 05.11.2025, Revised 02.02.2026, Accepted 24.02.2026 Published 26.03.2026

Retrieved from Vol. 12, No. 1, 2026

Suggested citation

Nester, O. (2026). Integration of artificial intelligence into the restoration of architectural monuments: Methods and prospects. Architectural Studies, 12(1), 93-105. https://doi.org/10.56318/as/1.2026.93

https://doi.org/10.56318/as/1.2026.93

Pages 93-105

References

  1. Abusaleh, S.W. (2024). Enhancing preservation outcomes for architectural heritage buildings through machine learning-driven future search optimization. Asian Journal of Civil Engineering, 25, 5277-5292. doi: 10.1007/s42107-024-01112-x.
  2. Bachmann-Gigl, U., & Dabiri, Z. (2024). Cultural heritage in times of crisis: Damage assessment in urban areas of Ukraine using Sentinel-1 SAR data. ISPRS International Journal of Geo-Information, 13(9), article number 319. doi: 10.3390/ijgi13090319.
  3. Buldo, M., Agustín-Hernández, L., & Verdoscia, C. (2024). Semantic enrichment of architectural heritage point clouds using artificial intelligence: The Palacio de Sástago in Zaragoza, Spain. Heritage, 7(12), 6938-6965. doi: 10.3390/heritage7120321.
  4. Cecere, L., Colace, F., Lorusso, A., Messina, B., Tucker, A., & Santaniello, D. (2024). IoT and digital twin: A new perspective for cultural heritage predictive maintenance. Procedia Structural Integrity, 64, 2181-2188. doi: 10.1016/j.prostr.2024.09.334.
  5. Chumak, O., & Gorkovchuk, J. (2023). Prior calculation of the accuracy of monitoring of cultural heritage objects using UAVs and laser scanning. Geodesy and Cartography, 49(3), 133-136. doi: 10.3846/gac.2023.18008.
  6. Cotella, V.A. (2023). From 3D point clouds to HBIM: Application of artificial intelligence in cultural heritage. Automation in Construction, 152, article number 104936. doi: 10.1016/j.autcon.2023.104936.
  7. Croce, V., Caroti, G., Piemonte, A., De Luca, L., & Véron, P. (2023). H-BIM and artificial intelligence: Classification of architectural heritage for semi-automatic scan-to-BIM reconstruction. Sensors, 23(5), article number 2497. doi: 10.3390/s23052497.
  8. Dragomir, O.E., & Dragomir, F. (2025). AI-based proactive maintenance for cultural heritage conservation: A hybrid neuro-fuzzy approach. Future Internet, 17(11), article number 510. doi: 10.3390/fi17110510.
  9. Felicetti, A., & Niccolucci, F. (2025). Artificial intelligence and ontologies for the management of heritage Digital Twins data. Data, 10(1), article number 1. doi: 10.3390/data10010001.
  10. Ge, Y., Guo, B., Zha, P., Jiang, S., Jiang, Z., & Li, D. (2024). 3D reconstruction of ancient buildings using UAV images and neural radiation field with depth supervision. Remote Sensing, 16(3), article number 473. doi: 10.3390/rs16030473.
  11. Gil, A., & Arayici, Y. (2025). Point cloud segmentation based on the Uniclass Classification System with random forest algorithm for cultural heritage buildings in the UK. Heritage, 8(5), article number 147. doi: 10.3390/heritage8050147.
  12. Gîrbacia, F. (2024). An analysis of research trends for using artificial intelligence in cultural heritage. Electronics, 13(18), article number 3738. doi: 10.3390/electronics13183738.
  13. Hosamo, H., & Mazzetto, S. (2025). Integrating knowledge graphs and digital twins for heritage building conservation. Buildings, 15(1), article number 16. doi: 10.3390/buildings15010016.
  14. International Charter for the Conservation and Restoration of Monuments and Sites. (The Venice Charter – 1964). (1964). Retrieved from https://civvih.icomos.org/wp-content/uploads/2022/03/Charter-of-Venice_1964.pdf.
  15. Karadag, İ. (2023). Machine learning for conservation of architectural heritage. Open House International, 48(1), 23-37. doi: 10.1108/OHI-05-2022-0124.
  16. Kotsiubivska, K., Tymoshenko, O., & Vasylevsky, A. (2024). Artificial Intelligence tools for preservation and popularization of cultural heritage. Digital Platform: Information Technologies in Sociocultural Sphere, 7(2), 275-282. doi: 10.31866/2617-796X.7.2.2024.317736.
  17. Kovalchuk, I., Kozhemiako, M., Kozhemiako, P., Kutsenko, O., & Matviichuk, D. (2025). Laser scanning and creation of a BIM model of the building No. 1 NTUU “Igor Sikorsky KPI” as a method of visualizing historical and architectural heritage. Land Management, Cadastre and Land Monitoring, 4, 82-94. doi: 10.31548/zemleustriy2025.04.06.
  18. Law of Ukraine No. 1805-III “On Protection of Cultural Heritage”. (2000, June). Retrieved from https://zakon.rada.gov.ua/laws/show/1805-14.
  19. Mansuri, L.E., & Patel, D.A. (2022). Artificial intelligence-based automatic visual inspection system for built heritage. Smart and Sustainable Built Environment, 11(3), 622-646. doi: 10.1108/SASBE-09-2020-0139.
  20. Microsoft. (n.d.). A virtual Renaissance for St. Peter’s Basilica thanks to AI. Retrieved from https://news.microsoft.com/source/emea/features/ia-renaissence-la-basilique-saint-pierre-en-3d/?lang=fr.
  21. Mishra, M. (2021). Machine learning techniques for structural health monitoring of heritage buildings: A state-of-the-art review and case studies. Journal of Cultural Heritage, 47, 227-245. doi: 10.1016/j.culher.2020.09.005.
  22. Mishra, M., & Lourenço, P.B. (2024). Artificial intelligence-assisted visual inspection for cultural heritage: State-of-the-art review. Journal of Cultural Heritage, 66, 536-550. doi: 10.1016/j.culher.2024.01.005.
  23. Ocón, D., Yin, C., & Luna, J. (2026). Artificial insights or historical fidelity? Crafting an ethical framework for the use of GenAI in the restoration, reconstruction and recreation of movable cultural heritage. AI & Society, 41, 121-134. doi: 10.1007/s00146-025-02454-z.
  24. Pasikowska-Schnass, M., & Lim, Y.-S. (2023). Artificial intelligence in the context of cultural heritage and museums. Complex challenges and new opportunities. European Parliament. Retrieved from https://www.europarl.europa.eu/RegData/etudes/BRIE/2023/747120/EPRS_BRI%282023%29747120_EN.pdf.
  25. Sharma, H. (2025). Applications of artificial intelligence and machine learning in the preservation and analysis of heritage structures: A comprehensive review. Archives of Computational Methods in Engineering, 33, 3001-3033. doi: 10.1007/s11831-025-10393-7.
  26. Shevchuk, T., & Tarasiuk, M. (2025). The role of artificial intelligence in the preservation of historical monuments. Problems of World History, 30, 183-190. doi: 10.46869/2707-6776-2025-30-10.
  27. Skamantzari, M., De Vos, P.-J., Revenko, S., & Ferreira, V.M. (2025). Advanced 3D documentation and capacity building for post-conflict damage assessment: The case of the Taras Shevchenko Chernihiv Regional Academic Music and Drama Theatre, Ukraine. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 68(9), 1373-1378. doi: 10.5194/isprs-archives-XLVIII-M-9-2025-1373-2025.
  28. Smith, B. (2025). Unlocking data to advance European commerce and culture. Microsoft. Retrieved from https://blogs.microsoft.com/on-the-issues/2025/07/20/eudigitalunlock/.
  29. Steering Committee for Culture, Heritage and Landscape (CDCPP). (2024). Guidelines given the latest technological developments, such as AI, complementing Council of Europe standards in the fields of culture, creativity and cultural heritage. Policy guidelines. Strasbourg: Culture and Heritage for Democracy Division, Democratic Institutions and Freedoms Department.
  30. Sugiyama, G., Bourgeois, I., & Rodrigues, H. (2025). A holistic methodology for the assessment of Heritage Digital Twin applied to Portuguese case studies. Digital Applications in Archaeology and Cultural Heritage, 36, article number e00390. doi: 10.1016/j.daach.2024.e00390.
  31. Tan, X., Xie, H., Xia, C., Ying, Y., Hokoi, S., & Li, Y. (2026). Artificial intelligence in five key phases of architectural heritage conservation: A systematic review. Journal of Building Engineering, 117, article number 114862. doi: 10.1016/j.jobe.2025.114862.
  32. UNESCO World Heritage Centre. (n.d.a). L’viv – the Ensemble of the Historic Centre. Retrieved from https://whc.unesco.org/en/list/865.
  33. UNESCO World Heritage Centre. (n.d.b). The Historic Centre of Odesa. Retrieved from https://whc.unesco.org/en/list/1703.
  34. United Nations Educational, Scientific and Cultural Organization. (2021). Recommendation on the ethics of artificial intelligence. Retrieved from https://www.unesco.org/en/legal-affairs/recommendation-ethics-artificial-intelligence.
  35. United Nations Educational, Scientific and Cultural Organization. (2024a). Action plan for culture in Ukraine. Retrieved from https://articles.unesco.org/sites/default/files/medias/fichiers/2024/09/UNESCO%20Action%20Plan%20Culture%20for%20Ukraine%20Recovery%202024.pdf.
  36. United Nations Educational, Scientific and Cultural Organization. (2024b). UNESCO assesses damage to cultural heritage after attack in Ukrainian city of L’viv. Retrieved from https://www.unesco.org/en/articles/unesco-assesses-damage-cultural-heritage-after-attack-ukrainian-city-lviv.
  37. United Nations Educational, Scientific and Cultural Organization. (2025). Ukraine: UNESCO steps up support for the World Heritage site in Odesa amid escalating damage. Retrieved from https://whc.unesco.org/en/news/2800.
  38. Vogel, B. (2025). AI and extended reality help to preserve built cultural heritage. ETH Zurich. Retrieved from https://ethz.ch/en/news-and-events/eth-news/news/2025/11/ai-and-extended-reality-help-to-preserve-built-cultural-heritage.html.
  39. Wagner, A., & de Clippele, M.-S. (2023). Safeguarding cultural heritage in the digital era – a critical challenge. International Journal for the Semiotics of Law, 36, 1915-1923. doi: 10.1007/s11196-023-10040-z.
  40. Wang, H., Gong, Y., Zhang, Y., & Li, F. (2025). Artificial intelligence for sustainable cultural heritage: Practical guidelines and case-based evidence. Sustainability, 17(20), article number 9192. doi: 10.3390/su17209192.
  41. Yiğit, A.Y., & Uysal, M. (2024). Automatic crack detection and structural inspection of cultural heritage buildings using UAV photogrammetry and digital twin technology. Journal of Building Engineering, 94, article number 109952. doi: 10.1016/j.jobe.2024.109952.
  42. Yu, T., et al. (2022). Artificial intelligence for Dunhuang cultural heritage protection: The project and the dataset. International Journal of Computer Vision, 130, 2646-2673. doi: 10.1007/s11263-022-01665-x.
  43. Zhang, M., Sun, S., & Li, Z., (2025). An automatic measurement method for architectural heritage based on point cloud semantic segmentation algorithm: A case study of the hollow watchtowers of the Ming Great Wall. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 68(9), 1691-1698. doi: 10.5194/isprs-archives-xlviii-m-9-2025-1691-2025.
ISSN 2411-801X e-ISSN 2786-7374  UDC 71;72
DOI: 10.56318/as