AI-Enhanced Real-Time Monitoring of Marine Pollution: Part 1 -A Stateof-the-Art and Scoping Review

Navya Prakash ^1* Oliver Zielinski ²

• ¹ Institute for Chemistry and Biology of the Marine Environment, Carl von Ossietzky University of Oldenburg, Oldenburg, Germany
• ² Leibniz Institute for Baltic Sea Research (LG), Warnemünde, Germany

Marine pollution, especially from oil spills and litter, poses significant threats to marine ecosystems, aquaculture, and fisheries. The proliferation of pollutants requires advanced monitoring techniques to enhance early detection and mitigation efforts. Artificial Intelligence revolutionises environmental monitoring by enabling rapid and precise pollution detection using remote sensing and machine learning models. This review synthesises 53 recent studies on Artificial Intelligence applications in marine pollution detection, focusing on different AI model architectures, sensing technologies, and pre-processing methods. The most deployed models of Random Forest, U-Network, Generative Adversarial Networks, Mask Region-based Convolution Neural Network, and You Only Look Once demonstrate high prediction accuracy for detecting oil spills and marine litter. However, challenges remain, including limited training datasets, inconsistencies in sensor data, and real-time monitoring constraints. Future research should improve Artificial Intelligence model generalisation, integrate multi-sensor data, and enhance real-time processing capabilities to create more efficient and scalable marine pollution detection systems.