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. The most efficient approach to detect, monitor, and tackle down looting activities relies on Earth Observation (EO) data exploitation. In critical areas, such as conflict zones, country borders, desert areas, remote regions, this technique can often be the only available to determine when, how, and where looting happens and 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 and recognise the typical features of looting activities.