[ALCEO] - Introduction

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

[ALCEO] - Gallery

[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 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, this method also enables the retrieval of information related to the 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 Artificial Intelligence methods for processing different types of EO data with the aim of semi-automatically identifying, in archaeological areas, 'anomalies' that can be recognised as looting pits.

The project will proceed by:

  1. Developing a pipeline for automatic dumping of EO datasets through open platforms
  2. Designing an 'anomaly'-detection algorithm on time series images, to detect looted sites
  3. Designing an experimental set-up for comparing, on a benchmark dataset, the proposed classification algorithm to the state-of-the-art ones.
  4. Archaeological ground-truthing in areas notoriously subject to looting activities, to verify automated identifications and test the accuracy of the predictions
  5. Producing a digital map of the identified looted sites to be provided to relevant authorities (Interpol, Carabinieri TPC).




Law Enforcement Agencies represent ALCEO's PRINCIPAL END-USERS

The project has been endorsed by:

  •  INTERPOL - International Criminal Police Organization 
    (Works of Art Unit) 

  •  MIC - Italian Ministry of Culture - 
    Coordinator of actions & investigations of the Carabinieri Command for the Protection of Cultural Heritage (Carabinieri TPC)




A novel system able to

  • provide a faster and reliable way to detect both past and ongoing illicit digging activities
  • monitor efficiently areas secluded or difficult to patrol (i.e., deserts, farmlands, etc.)
  • understand and follow the evolution of looters' behaviour by analysing the patterning of pits
  • facilitate the matching of recovered and seized archaeological items with their provenance




ALCEO Case Studies


Aquileia (UD) - Italy, Friuli Venezia Giulia

Arpinova (FG) - Italy, Puglia



ALCEO Kick-off Meeting!


ALCEO project is officially setting off.

During the Kick-Off Meeting, CCHT researchers will discuss with the European Space Agency's representatives the coming steps and the strategy toward successful results.






Principal Investigator:  

Arianna Traviglia, PhD, CCHT Coordinator


Marco Fiorucci, PhD, Research Fellow, Co-Investigator

Marina Ljubenovic, PhD, Research Fellow, Co-Investigator

Maria Cristina Salvi, Post-doc

Michela De Bernardin, Post-doc

Riccardo Giovanelli, PhD Student

Ayesha Anwar, PhD Student

Gregory Sech, Affiliated Researcher

Paolo Soleni, IT Technician & ML Engineer


[ALCEO] - Acknowledgments