Altaweel, Mark;
Khelifi, Adel;
Shana'ah, Mohammad;
(2023)
Monitoring Looting at Cultural Heritage Sites: Applying Deep Learning on Optical UAV Data as a Solution.
Social Science Computer Review
10.1177/08944393231188.
(In press).
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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.
| Type: | Article |
|---|---|
| Title: | Monitoring Looting at Cultural Heritage Sites: Applying Deep Learning on Optical UAV Data as a Solution |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1177/08944393231188 |
| Publisher version: | https://doi.org/10.1177/08944393231188 |
| Language: | English |
| Additional information: | © The Author(s) 2023. Creative Commons License (CC BY 4.0) This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
| Keywords: | heritage, archaeological sites, deep learning, security, looting, unmanned aerial vehicles, optical imagery |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL SLASH UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Institute of Archaeology UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Institute of Archaeology > Institute of Archaeology Gordon Square |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10171988 |
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