OPTIMAL · OPtimal Transport for Identifying Marauder Activities on LiDAR
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2022-01-16 → 2024-01-15
- EU contribution
- €184,117
- Participants
- 2
- Scheme
- MSCA-IF
Lines connect the coordinator with its partners.
Results in brief
OPtimal Transport for Identifying Marauder Activities on LiDAR
Illegal excavation of archaeological sites to collect historical material culture ("looting") is a pressing problem on a global scale with strong consequences on security, economics, and society. Looting is the main source of income for terroristic groups and organised crime undermining the security and development of the affected countries. The monitoring of looting (past and ongoing) thus plays a crucial role in the protection of cultural heritage by strengthening the ability of Law Enforcement Agencies to promptly react to criminal activities. Due to the spread of the phenomenon and the impossibility of physically inspecting unreachable areas (e.g., forests covered by thick and closed canopies) or hazardous zones, surveillance via remote sensing is the most efficient approach to monitor looting activities. The OPTIMAL (OPtimal Transport for Identifying Marauder Activities on LiDAR) project aims to undermine the illegal excavation of cultural heritage sites by developing an efficient and principled machine learning approach, based on optimal transport, to automatically detect past and present looting directly on airborne Light Detection And Ranging (LiDAR) point cloud time-series. Moving from this overall objective, OPTIMAL specifically intended to: O1. create the first multi-temporal LiDAR dataset to train change detection methods for the identification of looting activities; O2. implement a novel change detection method, based on optimal transport, for the automatic identification and monitoring of cultural heritage looted sites directly on LiDAR point cloud time series; O3. achieve a looting detection accuracy of 85% on two user-case scenarios relying on the ground-truthing data already collected and on the collaboration with landscape archaeologists. The project outcomes will concur to increase Europe's research profile in the current dominant discourse over the heritage safeguard by offering a powerful machine learning tool for archaeologists and stakeholders involved in the fight against marauder activities which represent a major source of income for criminal groups.
Data: CORDIS, © European Union
Project 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.""
Original text from CORDIS.
Participants
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Data: CORDIS, © European Union