UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Monitoring Looting at Cultural Heritage Sites: Applying Deep Learning on Optical UAV Data as a Solution

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). Green open access

[thumbnail of Altaweel_altaweel-et-al-2023-monitoring-looting-at-cultural-heritage-sites-applying-deep-learning-on-optical-unmanned-aerial.pdf]
Preview
Text
Altaweel_altaweel-et-al-2023-monitoring-looting-at-cultural-heritage-sites-applying-deep-learning-on-optical-unmanned-aerial.pdf

Download (1MB) | Preview

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
Downloads since deposit
25Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item