Project Description:
Anomalous vessel trajectories can signal a wide range of critical issues, from environmental threats like oil spills and biosecurity breaches to illegal fishing or vessels facing distress. Detecting these anomalies within the vast, dynamic maritime environment is a complex challenge. This project leverages machine learning techniques for maritime monitoring using Automatic Identification System (AIS) data. Our model tackles key challenges for real-world effectiveness:
- Dynamic Environments: The model adapts to shifting patterns driven by climate change, extreme weather, and changing global conditions.
- Context Matters: Detecting anomalies requires understanding normal behavior in different oceanic regions.
- Real-World Ready: The system leverages unlabeled data to remain effective in the face of limited ground truth information.
This project is carried out in collaboration with TAIAO and Starboard Maritime Intelligence.
Keywords: Maritime Anomaly Detection, Vessel Tracking, Continual Learning
