The key steps of the research methodology are detailed below.

• Create the first multi-temporal LiDAR dataset to train change-detection methods for the identification of looting activities, which will be released to the scientific community to serve as a benchmark for looting detection.
• Design of a change-detection algorithm on complex and time-evolving LiDAR point clouds via ML approach, OPT, based on optimal transport. The optimal transport theory provides a natural way to detect changes between pairs of point clouds allowing to compute the geometric discrepancy between two distributions (the pair of point clouds). The design and the implementation of this approach will be performed at the Kyoto University (Third Country host institution) under the supervision of Prof Makoto Yamada.
• Evaluation of the effectiveness of the OPT approach using both image-based manual inspection and fieldwork activities. To validate the accuracy of the predictions, the areas of interest will be visually inspected on screen for signs of looting by the CCHT archaeologists, led by Dr Arianna Traviglia, using available High Resolution (HR) satellite imagery. For a limited number of sites, a direct check on the ground will be performed through the support of a number of CCHT archaeologists and external collaborators that have committed to undertaking targeted surveys.