Deep Learning-Based Approaches for Oil Spill Detection: A Bibliometric Review of Research Trends and Challenges
Rodrigo Nogueira de Vasconcelos, André T. Cunha Lima, Carlos A. D. Lentini, José García Vivas Miranda, Luís Felipe Ferreira de Mendonça, José Marques Lopes, Mariana Martins Medeiros de Santana, Elaine Cristina Cambuí Barbosa, Deorgia Tayane Mendes de Souza, Diego P. Costa, Soltan Galano Duverger, Washington Franca-Rocha
Abstract
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.