Detecting fishing areas from navigation radar detector data

Detecting global fishing activity is essential for sustainable ocean governance, yet systems based on vessel-transmitted information, such as Automatic Identification System (AIS) and Vessel Monitoring Systems, are limited by access issues, coverage gaps, and the inability to detect non-cooperative vessels. To overcome these issues, this paper presents Point-to-Fishing (P2F), an AI-driven workflow to detect fishing areas and estimate fishing hours from Navigation Radar Detector (NRD) data of satellite or terrestrial systems, complemented with currents and bathymetry data from Copernicus and GEBCO. P2F integrates analytical components based on statistical analysis, machine learning, and deep learning to conduct vessel behaviour analysis, spatial feature extraction (vessel abundance, recurrence, current-driven interpolation, and bathymetric suitability), and anomaly detection. The workflow operates effectively with or without vessel identifiers, enabling the detection of fishing areas in data-sparse or AIS-denied regions, even using one satellite only. P2F is validated on data covering the North Sea, the Western Norwegian Sea, and the North Atlantic. The validation cases utilise terrestrial and satellite NRD data alternately, with the Global Fishing Watch fishing effort distributions as a validation reference. P2F achieves a consistent ~75% agreement in relevant fishing area classification and intense-fishing area identification, and ~93% accuracy in total fishing effort estimation.