[ALCEO] - Introduction

Automatic Looting Classification from Earth Observation (ALCEO)
Earth Observation and automated detection of looting activities of archaeological areas via AI approaches


[ALCEO] - Tab

Looting historical heritage is an illicit phenomenon that affects archaeological areas worldwide, particularly where surveillance is low, and access proves difficult. Carried out with different methods (by hand or mechanical support) and rates (systematic or casual), every looting enterprise leaves distinct marks on the terrain and damages the involved areas to a varying degree. A looted area could therefore display just a couple of holes or reveal a ‘moonlike’ scenario, full of deep and large craters, as in Dura Europos or Apamea (Syria).

Finding ways to detect, monitor, and tackle down looting activities and the subsequent illicit trafficking of antiquities is currently one of the major challenges in Cultural Heritage preservation. The most efficient approach relies on Earth Observation (EO) data exploitation. In critical areas, such as conflict zones, country borders, desert areas, and remote regions, this technique can often be the only one available to determine when, how, and where looting happens. By exploiting the potential of time-series images, it also enables to retrieve information related to shapes and patterns of the looting pits.

ALCEO project, in collaboration with and co-financed by the European Space Agency, aims to develop next-generation Artificial Intelligence methods to automatise the detection of looted sites on time series of EO data by building innovative Machine-Learning algorithms to fully exploit the large amount of data produced by satellite-based sensors and made available through platforms like Copernicus and USGS. By measuring the dissimilarities between consecutive satellite imagery, the system will be able to automatically detect the relevant ‘anomalies’ recognising typical patterns and features of looting activities.

ALCEO project will develop new methods based on Artificial Intelligence for processing different types of EO data to semi-automatic identify 'anomalies' recognisable as looting pits in archaeological areas. The project research agenda will proceed according to the following steps:
- Development of a pipeline for automatic dumping of EO datasets through open platforms.
- Design of an anomaly detection algorithm on time series images to detect looted sites.
- Design of an experimental set-up for comparing the proposed classification algorithm to the state-of-the-art ones on a benchmark dataset.
- Archaeological ground-truthing in areas where looting is known to happen, to verify automated identifications and test the accuracy of the predictions provided by the algorithm.
- Feeding the model with the data collected during ground-truthing activities.
- Comparison of performances achieved by the model with the ones produced by the retrained model on benchmark datasets.
- Producing a digital map of the identified looted sites to be provided to relevant authorities.

[ALCEO] - Funds


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