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

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...