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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Systematic Review

State of the Art: Analysis of Deep Learning Techniques in Images Acquired in an Aquatic Environment

by
Vanesa Lopez-Vazquez
1,2,
Geovanny Satama-Bermeo
1,
Hasan Issa Raheem
1 and
Jose Manuel Lopez-Guede
1,*
1
Department of Automatic Control and Systems Engineering, University of the Basque Country (UPV/EHU) Nieves Cano, 12, 01006 Vitoria-Gasteiz, Spain
2
Deusto SEIDOR, 01015 Vitoria-Gasteiz, Spain
*
Author to whom correspondence should be addressed.
Mach. Learn. Knowl. Extr. 2026, 8(5), 131; https://doi.org/10.3390/make8050131
Submission received: 6 March 2026 / Revised: 29 April 2026 / Accepted: 4 May 2026 / Published: 14 May 2026
(This article belongs to the Section Thematic Reviews)
Versions Notes

Abstract

The oceans and other marine ecosystems are indispensable to life, so the understanding and knowledge of their biodiversity is crucial to the use of their resources and exploration. These environments are complex and difficult to access, so different types of remote sensing technologies are used to study them. These intelligent sensors can collect a massive amount of data, which, once reviewed and analyzed, can help to draw conclusions and increase knowledge of these underwater environments. Manually reviewing and organizing through this large amount of information is both time-consuming and costly. Therefore, it is advisable to employ automated techniques from machine learning and deep learning fields. In recent years, these methods have proven to be efficient and have obtained very good results in solving different problems applied to the marine world: image enhancement, image classification, segmentation and object detection. This paper presents a systematic review, conducted in accordance with the PRISMA 2020 guidelines, aimed at summarizing the methods used to address underwater problems and their reported results.
Keywords: deep learning; underwater imagery; PRISMA review; image enhancement; species detection deep learning; underwater imagery; PRISMA review; image enhancement; species detection

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MDPI and ACS Style

Lopez-Vazquez, V.; Satama-Bermeo, G.; Raheem, H.I.; Lopez-Guede, J.M. State of the Art: Analysis of Deep Learning Techniques in Images Acquired in an Aquatic Environment. Mach. Learn. Knowl. Extr. 2026, 8, 131. https://doi.org/10.3390/make8050131

AMA Style

Lopez-Vazquez V, Satama-Bermeo G, Raheem HI, Lopez-Guede JM. State of the Art: Analysis of Deep Learning Techniques in Images Acquired in an Aquatic Environment. Machine Learning and Knowledge Extraction. 2026; 8(5):131. https://doi.org/10.3390/make8050131

Chicago/Turabian Style

Lopez-Vazquez, Vanesa, Geovanny Satama-Bermeo, Hasan Issa Raheem, and Jose Manuel Lopez-Guede. 2026. "State of the Art: Analysis of Deep Learning Techniques in Images Acquired in an Aquatic Environment" Machine Learning and Knowledge Extraction 8, no. 5: 131. https://doi.org/10.3390/make8050131

APA Style

Lopez-Vazquez, V., Satama-Bermeo, G., Raheem, H. I., & Lopez-Guede, J. M. (2026). State of the Art: Analysis of Deep Learning Techniques in Images Acquired in an Aquatic Environment. Machine Learning and Knowledge Extraction, 8(5), 131. https://doi.org/10.3390/make8050131

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