Next Article in Journal
Advances in Reconfigurable Vectorial Thrusters for Adaptive Underwater Robots
Previous Article in Journal
Numerical Investigation on Hydrodynamic Characteristics of Immersed Buoyant Platform
Article

Deep Learning for Deep Waters: An Expert-in-the-Loop Machine Learning Framework for Marine Sciences

1
Department of Computer Science and Engineering, University of Gothenburg | Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
2
Department of Mechanics and Maritime Science, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Current address: School, Inria, Université de Lille, CNRS, UMR 9189-CRIStAL, F-59000 Lille, France.
Academic Editor: Kristen Splinter
J. Mar. Sci. Eng. 2021, 9(2), 169; https://doi.org/10.3390/jmse9020169
Received: 4 December 2020 / Revised: 29 January 2021 / Accepted: 2 February 2021 / Published: 7 February 2021
(This article belongs to the Section Coastal Engineering)
Driven by the unprecedented availability of data, machine learning has become a pervasive and transformative technology across industry and science. Its importance to marine science has been codified as one goal of the UN Ocean Decade. While increasing amounts of, for example, acoustic marine data are collected for research and monitoring purposes, and machine learning methods can achieve automatic processing and analysis of acoustic data, they require large training datasets annotated or labelled by experts. Consequently, addressing the relative scarcity of labelled data is, besides increasing data analysis and processing capacities, one of the main thrust areas. One approach to address label scarcity is the expert-in-the-loop approach which allows analysis of limited and unbalanced data efficiently. Its advantages are demonstrated with our novel deep learning-based expert-in-the-loop framework for automatic detection of turbulent wake signatures in echo sounder data. Using machine learning algorithms, such as the one presented in this study, greatly increases the capacity to analyse large amounts of acoustic data. It would be a first step in realising the full potential of the increasing amount of acoustic data in marine sciences. View Full-Text
Keywords: machine learning; marine sciences; deep learning; expert-in-the-loop; turbulent ship wake; environmental impact of shipping machine learning; marine sciences; deep learning; expert-in-the-loop; turbulent ship wake; environmental impact of shipping
Show Figures

Figure 1

MDPI and ACS Style

Ryazanov, I.; Nylund, A.T.; Basu, D.; Hassellöv, I.-M.; Schliep, A. Deep Learning for Deep Waters: An Expert-in-the-Loop Machine Learning Framework for Marine Sciences. J. Mar. Sci. Eng. 2021, 9, 169. https://doi.org/10.3390/jmse9020169

AMA Style

Ryazanov I, Nylund AT, Basu D, Hassellöv I-M, Schliep A. Deep Learning for Deep Waters: An Expert-in-the-Loop Machine Learning Framework for Marine Sciences. Journal of Marine Science and Engineering. 2021; 9(2):169. https://doi.org/10.3390/jmse9020169

Chicago/Turabian Style

Ryazanov, Igor, Amanda T. Nylund, Debabrota Basu, Ida-Maja Hassellöv, and Alexander Schliep. 2021. "Deep Learning for Deep Waters: An Expert-in-the-Loop Machine Learning Framework for Marine Sciences" Journal of Marine Science and Engineering 9, no. 2: 169. https://doi.org/10.3390/jmse9020169

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop