| People | Locations | Statistics |
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| Mouftah, Hussein T. |
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| Dugay, Fabrice |
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| Rettenmeier, Max |
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| Tomasch, Ernst | Graz |
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| Cornaggia, Greta |
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| Palacios-Navarro, Guillermo |
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| Uspenskyi, Borys V. |
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| Khan, Baseem |
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| Fediai, Natalia |
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| Derakhshan, Shadi |
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| Somers, Bart | Eindhoven |
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| Anvari, B. |
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| Kraushaar, Sabine | Vienna |
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| Kehlbacher, Ariane |
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| Das, Raj |
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| Werbińska-Wojciechowska, Sylwia |
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| Brillinger, Markus |
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| Eskandari, Aref |
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| Gulliver, J. |
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| Loft, Shayne |
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| Kud, Bartosz |
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| Matijošius, Jonas | Vilnius |
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| Piontek, Dennis |
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| Kene, Raymond O. |
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| Barbosa, Juliana |
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Altaweel, Mark
University College London
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (5/5 displayed)
- 2023Monitoring Looting at Cultural Heritage Sites: Applying Deep Learning on Optical Unmanned Aerial Vehicles Data as a Solutioncitations
- 2023Monitoring Looting at Cultural Heritage Sites: Applying Deep Learning on Optical UAV Data as a Solution
- 2022Automated Archaeological Feature Detection Using Deep Learning on Optical UAV Imagery: Preliminary Results
- 2022Automated Archaeological Feature Detection Using Deep Learning on Optical UAV Imagery: Preliminary Resultscitations
- 2021The structure, centrality, and scale of urban street networks: cases from Pre-Industrial Afro-Eurasiacitations
Places of action
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article
Monitoring Looting at Cultural Heritage Sites: Applying Deep Learning on Optical UAV Data as a Solution
Abstract
The looting of cultural heritage sites has been a growing problem and threatens national economies, social identity, destroys research potential, and traumatizes communities. For many countries, the challenge in protecting heritage is that there are often too few resources, particularly paid site guards, while sites can also be in remote locations. Here, we develop a new approach that applies deep learning methods to detect the presence of looting at heritage sites using optical imagery from unmanned aerial vehicles (UAVs). We present results that demonstrate the accuracy, precision, and recall of our approach. Results show that optical UAV data can be an easy way for authorities to monitor heritage sites, demonstrating the utility of deep learning in aiding the protection of heritage sites by automating the detection of any new damage to sites. We discuss the impact and potential for deep learning to be used as a tool for the protection of heritage sites. How the approach could be improved with new data are also discussed. Additionally, the code and data used are provided as part of the outputs.
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