POSEatSea: A Hybrid Sentinel-1 SAR and AIS-Based Framework for Predictive Oil Spill Risk Detection at Sea
Keywords:
AIS data, Copernicus data, Machine learning, Maritime risk analysis, Oil spill detection, Remote sensing, Sentinel-1 SARAbstract
Oil spill monitoring at sea is traditionally performed using remote sensing systems that detect surface contamination after an event has already occurred. To move from reactive detection to proactive risk assessment, this paper proposes POSEatSea, a hybrid framework that combines Sentinel-1 SAR imagery from Copernicus with AIS-based vessel behavior analysis. Sentinel-1 provides all-weather, day-and-night radar imagery for detecting spill-like ocean surface anomalies, while AIS data helps identify suspicious vessel movement patterns such as route deviations, unusual loitering, and irregular motion that may indicate elevated spill risk. The proposed workflow includes satellite image preprocessing, AIS cleaning, feature extraction, and machine learning-based classification to distinguish normal and risky maritime conditions. By fusing both data sources, the framework improves situational awareness and strengthens early warning capability for maritime authorities. The model is designed to support faster intervention, reduce environmental damage, and enhance monitoring of high-risk shipping corridors.
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