Deep Learning for Deep Waters: An Expert-in-the-Loop Machine Learning Framework for Marine Sciences
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 a...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/9/2/169 |
| _version_ | 1797413273620971520 |
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| author | Igor Ryazanov Amanda T. Nylund Debabrota Basu Ida-Maja Hassellöv Alexander Schliep |
| author_facet | Igor Ryazanov Amanda T. Nylund Debabrota Basu Ida-Maja Hassellöv Alexander Schliep |
| author_sort | Igor Ryazanov |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T05:15:34Z |
| format | Article |
| id | doaj.art-00ba844a542048adb63b923694161582 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-09T05:15:34Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-00ba844a542048adb63b9236941615822023-12-03T12:45:48ZengMDPI AGJournal of Marine Science and Engineering2077-13122021-02-019216910.3390/jmse9020169Deep Learning for Deep Waters: An Expert-in-the-Loop Machine Learning Framework for Marine SciencesIgor Ryazanov0Amanda T. Nylund1Debabrota Basu2Ida-Maja Hassellöv3Alexander Schliep4Department of Computer Science and Engineering, University of Gothenburg | Chalmers University of Technology, SE-412 96 Gothenburg, SwedenDepartment of Mechanics and Maritime Science, Chalmers University of Technology, SE-412 96 Gothenburg, SwedenDepartment of Computer Science and Engineering, University of Gothenburg | Chalmers University of Technology, SE-412 96 Gothenburg, SwedenDepartment of Mechanics and Maritime Science, Chalmers University of Technology, SE-412 96 Gothenburg, SwedenDepartment of Computer Science and Engineering, University of Gothenburg | Chalmers University of Technology, SE-412 96 Gothenburg, SwedenDriven 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.https://www.mdpi.com/2077-1312/9/2/169machine learningmarine sciencesdeep learningexpert-in-the-loopturbulent ship wakeenvironmental impact of shipping |
| spellingShingle | Igor Ryazanov Amanda T. Nylund Debabrota Basu Ida-Maja Hassellöv Alexander Schliep Deep Learning for Deep Waters: An Expert-in-the-Loop Machine Learning Framework for Marine Sciences Journal of Marine Science and Engineering machine learning marine sciences deep learning expert-in-the-loop turbulent ship wake environmental impact of shipping |
| title | Deep Learning for Deep Waters: An Expert-in-the-Loop Machine Learning Framework for Marine Sciences |
| title_full | Deep Learning for Deep Waters: An Expert-in-the-Loop Machine Learning Framework for Marine Sciences |
| title_fullStr | Deep Learning for Deep Waters: An Expert-in-the-Loop Machine Learning Framework for Marine Sciences |
| title_full_unstemmed | Deep Learning for Deep Waters: An Expert-in-the-Loop Machine Learning Framework for Marine Sciences |
| title_short | Deep Learning for Deep Waters: An Expert-in-the-Loop Machine Learning Framework for Marine Sciences |
| title_sort | deep learning for deep waters an expert in the loop machine learning framework for marine sciences |
| topic | machine learning marine sciences deep learning expert-in-the-loop turbulent ship wake environmental impact of shipping |
| url | https://www.mdpi.com/2077-1312/9/2/169 |
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