Project description
Cracking down on looting of archaeological sites
The upsurge of illegal excavation of archaeological sites and the subsequent trafficking of antiquities is a multibillion euro industry that has gathered pace in connection, for example, to turmoil in Middle East. Moreover, the looting of archaeological sites has exacerbated during the COVID-19 pandemic. The EU-funded OPTIMAL project will develop an automatic means of identifying looting activities. It will take a machine learning approach based on optimal transport to automatically detect looting (past and present) directly on airborne lidar time series. The project will also assemble lidar data set and make them publicly available, to be used free of charge for identifying illegal activities.
Objective
"Illegal excavation of archaeological sites aimed at collecting historical material culture (""looting"") is a pressing problem on a global scale. The global upsurge of in the illegal excavation of cultural heritage sites (e.g. in connection to turmoils in Middle East or due to the impossibility of monitoring inaccessible areas, like in South America) and the subsequent trafficking of antiquities, exacerbated by the Covid lockdown, calls for the timely development of automatic means for identifying looting activities. The OPTIMAL (OPtimal Transport for Identifying Marauder Activities on LiDAR) project aims to tackle this challenge by developing an efficient and principled Machine Learning (ML) approach based on Optimal Transport to automatically detect looting (past and present) directly on airborne Light Detection And Ranging (LiDAR) point cloud time-series. OPTIMAL proposes, for the first time, the use of LiDAR for monitoring and assessing the damages of looting based on LiDAR’s unique ability to penetrate forest canopies and enabling to see a range of looting-related features under the canopy (e.g. shape and depth of the lootings pits) that otherwise would remain hidden due to vegetation covers. OPTIMAL will create and make publicly available the first multi-temporal LiDAR dataset for illegal activities’ identification to foster the interest of MLs researchers in developing new methods to tackle challenges in landscape archaeology and to evaluate the developed ML approach. Results of this interdisciplinary research will be widely disseminated within Cultural Heritage, Remote Sensing and Machine Learning communities and to others that can exploit OPTIMAL’s results. A communication strategy will be designed to ignite enthusiasm for technological advancements for the protection of our Heritage."
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- natural sciencescomputer and information sciencesartificial intelligencecomputer vision
- engineering and technologyenvironmental engineeringremote sensing
- humanitieshistory and archaeologyarchaeology
- natural sciencesmathematicsapplied mathematicsstatistics and probability
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
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Programme(s)
Funding Scheme
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinator
16163 Genova
Italy