Title: Environmental crimes’ intelligence and investigation protocol based on multiple data sources
Grant agreement no: 101073874
Duration: 1 September 2022 – 31 August 2025
The aim of the EMERITUS project is to realize and implement a protocol for effective environmental crime investigation, leveraging on the integration of innovative monitoring and analysis technologies (drones, satellite data, virtual sensors, geo-intelligence data, etc.), and on a complementary training programme aimed at fostering environmental enforcement authorities (e.g. Police Authorities and Border Guards Authorities) intelligence and investigation capabilities, at national and cross-border level. In order to achieve this goal, the EMERITUS consortium has gathered 8 Police/Border Guard authorities from 5 countries, 4 security experts, 2 training specialists and 6 technological partners, that will be the core of the platform. The consortium will focus on the co-creation of an investigation protocol centered on environmental crimes investigation, that will orient to the creation of the geo-intelligence platform. This platform will integrate several monitoring and investigation technologies and data sources, in a single view to support Police Authorities and decisionmakers.
Secondly, a new training programme for Police Authorities and other security practitioners, focused on environmental (waste-related) crimes investigation and prevention, will be delivered combining both theoretical aspects and hand-on simulations to empower end-users to actually use the platform and related technologies. Such exercises will serve to validate both the platform and the protocol, via simulations based on 4 realistic use cases (heterogeneous and of growing complexity), taking advantage of the shared will of Police Authorities and Border Guards to focus on ad hoc demonstration sites, with the additional availability of partners’ simulation facilities. The validation of the platform and of the protocol will result in the successful elaboration of a set of evidence-based recommendations for policy authorities and decision-makers.
This project has received funding from the European Union’s H2020 research & innovation programme under Grant Agreement no 101073874