- 1School of Computing and Engineering, University of West London, London, United Kingdom of Great Britain – England, Scotland, Wales (tesfaye.temtimetessema@uwl.ac.uk)
- 2The Faringdon Research Centre for Non-Destructive Testing and Remote Sensing, University of West London, London, United Kingdom of Great Britain – England, Scotland, Wales
- 3Information School, The University of Sheffield, Sheffield, S10 2TN, United Kingdom of Great Britain – England, Scotland, Wales
- 4Signal Processing for Telecommunications and Economics Lab., Roma Tre University, Rome, Italy
Cultural heritage (CH) sites face escalating threats from environmental degradation, climate change, urbanization, and human activity. While traditional methods such as in-situ measurements and drone surveys using LiDAR and photogrammetry are valuable, they are often constrained by limited spatial coverage, revisit times, and operational challenges. To address these gaps, the integration of satellite remote sensing and artificial intelligence (AI) offers a transformative solution for scalable, continuous monitoring and automated change detection [1].
This study explores the combined use of multi-temporal satellite imagery—both optical and radar—and AI-driven algorithms to monitor structural changes and assess the environmental impacts on CH sites. By employing machine learning and deep learning models, the research enhances detection efficiency and accuracy, enabling non-invasive identification of structural deterioration, environmental stresses, and long-term degradation [2]. The approach emphasises using publicly available datasets and open-source tools to ensure accessibility and scalability.
In addition to technological advancements, the study adopts an ethical AI framework to address cultural and historical biases in CH monitoring. This framework seeks to minimise risks such as misrepresentation of marginalized communities and challenges posed by digitisation, including concerns about authenticity and the artificial reproduction of heritage assets. By integrating ethical considerations into the development and deployment of AI models, the research ensures that technological solutions align with sustainable and inclusive preservation practices.
The findings underscore the potential of combining advanced remote sensing technologies with AI to foster interdisciplinary collaboration, improve monitoring methodologies, and inform ethical policy frameworks. This integrated approach aims to safeguard cultural heritage sites for future generations.
Keywords: Cultural Heritage, Remote Sensing, Artificial Intelligence, Machine Learning, Monitoring
Acknowledgements
The Authors would like to express their sincere thanks and gratitude to the following trusts, charities, organisations and individuals for their generosity in supporting this project: Lord Faringdon Charitable Trust, The Schroder Foundation, Cazenove Charitable Trust, Ernest Cook Trust, Sir Henry Keswick, Ian Bond, P. F. Charitable Trust, Prospect Investment Management Limited, The Adrian Swire Charitable Trust, The John Swire 1989 Charitable Trust, The Sackler Trust, The Tanlaw Foundation, and The Wyfold Charitable Trust.
References
[1] Cuca, B., Zaina, F., & Tapete, D. (2023). Monitoring of Damages to Cultural Heritage across Europe Using Remote Sensing and Earth Observation: Assessment of Scientific and Grey Literature. Remote Sensing, 15(15), 3748.
[2] Argyrou, A., & Agapiou, A. (2022). A Review of Artificial Intelligence and Remote Sensing for Archaeological Research. Remote Sensing, 14(23), 6000.
How to cite: Tessema, T., Motavallizadeh Naeini, M., Azarmehr, N., Benedetto, F., and Tosti, F.: Integrating Satellite Remote Sensing and Ethical AI for Cultural Heritage Preservation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4477, https://doi.org/10.5194/egusphere-egu25-4477, 2025.
