Deep Learning-Based Approaches for Oil Spill Detection: A Bibliometric Review of Research Trends and Challenges

• Published in: Journal of marine science and engineering Vol. 11; no. 7; p. 1406
• Main Authors: Vasconcelos, Rodrigo N., Lima, André T. Cunha, Lentini, Carlos A. D., Miranda, José Garcia V., de Mendonça, Luís F. F., Lopes, José M., Santana, Mariana M. M., Cambuí, Elaine C. B., Souza, Deorgia T. M., Costa, Diego P., Duverger, Soltan G., Franca-Rocha, Washington S.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2023
• Subjects: Algorithms; Analysis; Artificial neural networks; Bibliometrics; Biodiversity; Citations; Collaboration; Computer architecture; Cooperation; Data acquisition; Deep learning; Detection; Environmental impact; Geospatial data; Information processing; Keywords; Literature reviews; Machine learning; Marine ecosystems; Multilayer perceptrons; Neural networks; Oil spills; Pollution detection; Remote sensing; Research methods; Researchers; SAR; Scientometrics; Semantics; Trends; Brazil; United Kingdom
• ISSN: 2077-1312; 2077-1312
• DOI: 10.3390/jmse11071406

Oil spill detection and mapping using deep learning (OSDMDL) is crucial for assessing its impact on coastal and marine ecosystems. A novel approach was employed in this study to evaluate the scientific literature in this field through bibliometric analysis and literature review. The Scopus database was used to evaluate the relevant scientific literature in this field, followed by a bibliometric analysis to extract additional information, such as architecture type, country collaboration, and most cited papers. The findings highlight significant advancements in oil detection at sea, with a strong correlation between technological evolution in detection methods and improved remote sensing data acquisition. Multilayer perceptrons (MLP) emerged as the most prominent neural network architecture in 11 studies, followed by a convolutional neural network (CNN) in 5 studies. U-Net, DeepLabv3+, and fully convolutional network (FCN) were each used in three studies, demonstrating their relative significance too. The analysis provides insights into collaboration, interdisciplinarity, and research methodology and contributes to the development of more effective policies, strategies, and technologies for mitigating the environmental impact of oil spills in OSDMDL.