AI-BASED PREDICTION OF CULTURAL HERITAGE ARTIFACT DETERIORATION DUE TO WEATHER CONDITIONS IN INDIA
DOI:
https://doi.org/10.29121/shodhkosh.v5.i4.2024.4783Keywords:
Cultural Heritage, Machine Learning, Artificial Intelligenc, Prediction, Augmented RealityAbstract [English]
The preservation of cultural heritage artifacts is a critical concern, particularly in a country like India, where diverse climatic conditions—including extreme temperatures, humidity variations, and pollution—can accelerate their deterioration. Traditional conservation techniques, while effective, often lack the predictive capabilities necessary to mitigate potential damage proactively. Recent advancements in artificial intelligence (AI) have opened new possibilities for enhancing heritage conservation by forecasting environmental threats and deterioration patterns.
This review paper explores scholarly research published between 2019 and 2022 on AI applications in predicting and mitigating the degradation of cultural artifacts in India. It examines key methodologies such as machine learning algorithms, deep learning models, and sensor-based AI systems used to analyze weather patterns, air quality, and material degradation. The paper also discusses challenges in AI-driven conservation, including data availability, model accuracy, and the integration of AI with existing heritage management practices.Despite these challenges, AI-driven approaches offer significant potential for improving the efficiency and precision of conservation efforts. By providing early warnings and predictive insights, AI can aid heritage professionals in making informed decisions to preserve historical artifacts more effectively.
References
Liu, Y., Wang, Y., & Liu, C. (2022). A Deep-Learning Method Using Auto-encoder and Generative Adversarial Network for Anomaly Detection on Ancient Stone Stele Surfaces. arXiv preprint arXiv:2308.04426.
Mishra, M., Barman, T., & Ramana, G. V. (2022). Artificial intelligence-based visual inspection system for structural health monitoring of cultural heritage. Journal of Civil Structural Health Monitoring, 14, 103–120. DOI: https://doi.org/10.1007/s13349-022-00643-8
Boesgaard, C., Hansen, B. V., & Torp-Smith, N. (2022). Prediction of the indoor climate in cultural heritage buildings through machine learning: first results from two field tests. Heritage Science, 10, 176. DOI: https://doi.org/10.1186/s40494-022-00805-3
Paul, A. J., Ghose, S., Aggarwal, K., Nethaji, N., Pal, S., & Purkayastha, A. D. (2021). Machine Learning Advances aiding Recognition and Classification of Indian Monuments and Landmarks. arXiv preprint arXiv:2107.14070. DOI: https://doi.org/10.1109/UPCON52273.2021.9667619
Abate, D., Paolanti, M., Pierdicca, R., Lampropoulos, A., Toumbas, K., Agapiou, A., Vergis, S., Malinverni, E., Petrides, K., Felicetti, A. et al., 2022. Significance. Stop Illicit Heritage Trafficking with Artificial Intelligence. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 729–736. DOI: https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-729-2022
Abgaz, Y., Rocha Souza, R., Methuku, J., Koch, G., Dorn, A., 2021. A methodology for semantic enrichment of cultural heritage images using artificial intelligence technologies. Journal of Imaging, 7(8), 121. DOI: https://doi.org/10.3390/jimaging7080121
Acke, L., De Vis, K., Verwulgen, S., Verlinden, J., 2021. Survey and literature study to provide insights on the application of 3D technologies in objects conservation and restoration. Journal of Cultural Heritage, 49, 272–288. DOI: https://doi.org/10.1016/j.culher.2020.12.003
Argyrou, A., Agapiou, A., 2022. A Review of Artificial Intelligence and Remote Sensing for Archaeological Research. Remote Sensing, 14(23), 6000. Benjamin, W., 2017. The work of art in the age of mechanical reproduction. Aesthetics, Routledge, 66–69. Boast, R., 2011. Neocolonial collaboration: Museum as contact zone revisited. Museum anthropology, 34(1), 56–70. DOI: https://doi.org/10.1111/j.1548-1379.2010.01107.x
Camara, A., 2020. International council of museums (icom): Code of ethics. Encyclopedia of Global Archaeology, Springer, 5868–5872. Cohen, I., Evgeniou, T., Gerke, S., Minssen, T., 2020. The European artificial intelligence strategy: implications and challenges for digital health. The Lancet Digital Health, 2, e376- e379. DOI: https://doi.org/10.1016/S2589-7500(20)30112-6
Espina-Romero, L., Guerrero-Alcedo, J., 2022. Fields Touched by Digitalization: Analysis of Scientific Activity in Scopus. Sustainability, 14(21), 14425. European Commission, 2020. White paper on artificial intelligence: a european approach to excellence and trust. White Paper COM(2020) 65 final, European Commission, Brussels. European Parliament, Council of the European Union, n.d. Regulation (EU) 2016/679 of the European Parliament and of the Council.
Felicetti, A., Paolanti, M., Zingaretti, P., Pierdicca, R., Malinverni, E. S., 2021. Mo. Se.: Mosaic image segmentation based on deep cascading learning. Virtual Archaeology Review, 12(24), 25–38. DOI: https://doi.org/10.4995/var.2021.14179
Floridi, L., 2019. What the near future of artificial intelligence could be. Philosophy & Technology, 32, 1–15. DOI: https://doi.org/10.1007/s13347-019-00345-y
Granata, F., Di Nunno, F., 2021. Artificial Intelligence models for prediction of the tide level in Venice. Stochastic Environmental Research and Risk Assessment, 35(12), 2537–2548 DOI: https://doi.org/10.1007/s00477-021-02018-9
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Yogesh Patel, Krunal Suthar, Mitul Patel, Harshad Chaudhary

This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.