Project description
ML and GeoAI models to improve Copernicus surveillance
The Copernicus Security Service (CSS) plays a vital role in supporting the EU’s security, safety, law enforcement, and international commitments, with ongoing efforts from the European Commission to enhance its capabilities. The EU-funded AI4COPSEC project will leverage advanced machine learning (ML) and GeoAI models to enhance Copernicus products and services. Specifically, it will improve oil spill response, illegal fishing alerts, and develop new services for search-and-rescue operations and irregular migration detection. By using self-supervised deep learning, geomatics, and open-source intelligence (OSINT) data from multiple sources, the project aims to boost the timeliness and accuracy of information from both Earth observation (EO) and non-EO data. It will also focus on detecting anomalies in maritime traffic and improving small vessel detection.
Objective
The Copernicus Security Service (CSS) is instrumental to support the European Union’s security, safety, law enforcement, and international commitments with the European Commission advocating for its advancement. The AI4COPSEC project aims to demonstrate the potential of advanced ML and GeoAI models to provide relevant intelligence for enhancing existing Copernicus products and services such as oil spill response, illegal fishing alert and create new services intended to support Search-and–Rescue operations and irregular migration detection. The project will enhance CSS services through the usage of self-supervised deep learning models, geomatics and social media data (OSINT) extracted from heterogenous multi-sources data. AI4COPSEC is expected to have a transformative effect on the operational capabilities of EU security,surveillance and safety by leveraging advances to enhance the timeliness, accuracy, and relevance of information derived from EO and non-EO data sources, including the innovative use of thermal imagery for ship detection, the combination of satellite automatic identification system (AIS) data with structured data extracted from social media sources and the usage of in-situ environmental measurement derived from AIS data and IoT devices. This integration will lead to more effective and efficient security operations, environmental monitoring, and disaster response. AI4COPSEC is also set to detect anomalies and threat in the maritime traffic and advance ship detection capabilities, utilising high-resolution optical images and advanced segmentation algorithms to enhance the detection of small vessels independently of the type of material (metallic structures, wood, rubber...) a critical factor for a comprehensive maritime surveillance in line with the CSS and the EU's maritime security strategy (EUMSS).
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
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HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinator
0164 Oslo
Norway