Deep learning for deep waters: An expert-in-the-loop machine learning framework for marine sciences
Journal article, 2021

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.

Turbulent ship wake

Marine sciences

Environmental impact of shipping

Deep learning

Expert-in-the-loop

Machine learning

Author

Igor Ryazanov

University of Gothenburg

Amanda Nylund

Chalmers, Mechanics and Maritime Sciences, Maritime Studies, Maritime Environmental Sciences

Debabrota Basu

University of Gothenburg

Ida-Maja Hassellöv

Chalmers, Mechanics and Maritime Sciences, Maritime Studies, Maritime Environmental Sciences

Alexander Schliep

University of Gothenburg

Journal of Marine Science and Engineering

20771312 (eISSN)

Vol. 9 2 1-18 169

Subject Categories

Other Computer and Information Science

Language Technology (Computational Linguistics)

Media Engineering

DOI

10.3390/jmse9020169

More information

Latest update

3/18/2021