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AI-enhanced real-time monitoring of marine pollution: part 1-A state-of-the-art and scoping review
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A review of 53 studies on AI applications in marine pollution monitoring finds that models like Random Forest, U-Net, and YOLO achieve high accuracy in detecting oil spills and marine litter from remote sensing data. Key challenges remain, including limited training datasets and real-time processing constraints that must be addressed for scalable deployment.
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 revolutionizes environmental monitoring by enabling rapid and precise pollution detection using remote sensing and machine learning models. This review synthesizes 53 recent studies on Artificial Intelligence applications in marine pollution detection, focusing on different model architectures, sensing technologies and preprocessing methods. The most deployed models of Random Forest, U-Network, Generative Adversarial Networks, Mask Region-based Convolution Neural Network and You Only Look Once demonstrated high prediction rate 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 generalization, integrate multi-sensor data and enhance real-time processing capabilities to create more efficient and scalable marine pollution detection systems.
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The Diversity of Artificial Intelligence Applications in Marine Pollution: A Systematic Literature Review
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This systematic literature review analyzed AI applications for marine pollution management from Web of Science and Scopus databases, finding that 57% target monitoring, 24% management, and 19% prediction. The review identifies oil spill detection, water quality monitoring, and plastic identification as the three major application areas where AI offers the greatest capabilities.
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This review assessed how artificial intelligence technologies—including satellite image analysis, computer vision, and machine learning—are being applied to monitor ocean plastic pollution. The authors found AI can dramatically expand spatial coverage and detection speed compared to traditional ship-based surveys, though ground-truth validation and data standardization remain challenges.
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