Monitoring Looting at Cultural Heritage Sites: Applying Deep Learning on Optical Unmanned Aerial Vehicles Data as a Solution
| dc.contributor.author | Khelifi, Adel | |
| dc.contributor.author | Altaweel, Mark | |
| dc.contributor.author | Shana'ah, Mohammad | |
| dc.date.accessioned | 2024-05-06T07:42:49Z | |
| dc.date.available | 2024-05-06T07:42:49Z | |
| dc.date.issued | 2023-07-10 | |
| dc.description | The looting and destruction of heritage sites is a growing concern for authorities and communities worldwide (Al-Ansi et al., 2021; Basnet Silwal, 2021). The consequences of such damage can have far-reaching social impact, including damage to local economies that depend on heritage tourism, destruction of sites used for scientific and heritage research, attacks on ethnic or religious identity affiliated with heritage places, and the erasure of the past that can be important for social cohesion and community building (Kersel and Hill 2020; Brusasco, 2012; Taniguchi 2017). Additionally, looted objects can be sold on the black market, resulting in their permanent loss and even funding of other illegal activities that can harm societies where looting might be prevalent (Clarke & Szydlo, 2017). Given these threats to cultural heritage sites and their broader social and economic implications, there is a critical need for new technologies that can enhance site protection. Keywords: heritage, archaeological sites, deep learning, security, looting, unmanned aerial vehicles, optical imagery | |
| dc.description.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 is also discussed. Additionally, the code and data used are provided as part of the outputs. Keywords: Monitoring Looting, Deep Learning, Code, Optical UAV data | |
| dc.identifier.citation | Altaweel, M., Khelifi, A., & Shana’ah, M. M. (2024). Monitoring Looting at Cultural Heritage Sites: Applying Deep Learning on Optical Unmanned Aerial Vehicles Data as a Solution. Social Science Computer Review, 42(2), 480-495. | |
| dc.identifier.doi | https://doi.org/10.1177/08944393231188471 | |
| dc.identifier.uri | https://dspace.adu.ac.ae/handle/1/5259 | |
| dc.language.iso | en | |
| dc.publisher | Sage Journals | |
| dc.title | Monitoring Looting at Cultural Heritage Sites: Applying Deep Learning on Optical Unmanned Aerial Vehicles Data as a Solution | |
| dc.type | Article |