Maritime Surveillance

Maritime surveillance aims to enhance situational awareness and safety at sea by monitoring vessel activity and detecting anomalies. Current research heavily utilizes deep learning, employing architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs, including LSTMs and GRUs), and transformer models for tasks such as vessel detection, tracking, re-identification, and trajectory prediction, often incorporating data fusion from multiple sources (AIS, radar, optical and thermal imagery). These advancements improve accuracy and efficiency in detecting illegal activities (e.g., IUU fishing, smuggling), enhancing maritime security and contributing to more effective resource management and environmental protection.