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The supporting role of Artificial Intelligence and Machine/Deep Learning in monitoring the marine environment: a bibliometric analysis
Summary
This review examines the supporting role of artificial intelligence and machine learning in monitoring and managing plastic pollution, covering applications in remote sensing, image-based plastic detection, and predictive modeling of plastic fate. The authors identify deep learning for image classification and satellite-based detection as the most rapidly advancing AI applications in plastic pollution science.
The widespread interest towards a sustainable and effective monitoring of the environment is increasingly demanding the development of modern and more affordable technologies to support or even replace the traditional time-consuming, high-cost sampling surveys at a multi-scale level. Researchers are highly benefitting from the recent enormous progresses achieved in the Artificial Intelligence (AI) field, with Machine/Deep Learning (ML/DL) applications increasing at sight. This gives a remarkable contribution to the environmental monitoring at sea, further allowing to develop efficient, smart and low-cost solutions to support the wide variety of tasks dealing with this objective. This study explores the global scientific literature on AI and ML/DL applications for the environmental monitoring over the last years. The VOSviewer software has been used to create maps based on the bibliographic network data: this allowed to display the relationships among scientific journals, researchers, and countries and to analyze the co-occurrence of different terms connected to the research. The resulting bibliometric analysis aims at verifying the major research interests and at providing the community with interesting findings and new perspectives on this very important topic, highlighting the great potential and flexibility of these methodologies and the excellent achievements they obtained in the last years.
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