Small-scale fisheries, despite their biological, economic, and societal importance—especially in the Mediterranean—often lack quantitative and spatial data. EU regulations now require member states to track small-scale vessel movements under the Common Fisheries Policy to better manage fishing activities.
Technological advances have made fishing a major threat to marine biodiversity. In the context of CSS enhancement, AI4COPSEC addresses the challenges of Illegal, Unreported, and Unregulated (IUU) fishing by integrating non-cooperative remote sensing technologies and ship-reporting systems, including electronic monitoring (EM), to provide a detailed overview of fishing activities and support transparent fisheries management.
AI4COPSEC focuses on fusing multi-source and multi-sensor data to monitor maritime activities, particularly fishing vessels. GeoAI algorithms, applied to SAR, optical, and thermal imagery, combined with time-series analysis from ship reporting data, will classify vessels based on geometry and movement patterns. Machine learning models, including self-supervised algorithms, will enhance classification accuracy.
Ship reporting data such as AIS (for large-scale fisheries) and GPS-loggers (for small-scale fisheries) will be processed and synchronized with image-based detections. To identify “dark” vessels avoiding location broadcasts, SAR, optical, and thermal imagery will be used and correlated with AIS, VMS, and LRIT data, improving the detection of illegal fishing activities.
The use case will employ Sentinel and CCM imagery from ESA, focusing on specific study areas in the Adriatic, Ionian, and Tyrrhenian Seas, targeting protected areas, islands, and fish farms.
