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The Application of Deep Learning in Marine Sciences

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Deep Learning: Algorithms and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 865))

Abstract

Ecological studies are increasingly using video image data to study the distribution and behaviour of organisms. Particularly in marine sciences cameras are utilised to access underwater environments. Up till now image data has been processed by human observers which is costly and often represents repetitive mundane work. Deep learning techniques that can automatically classify objects can increase the speed and the amounts of data that can be processed. This ultimately will make image processing in ecological studies more cost effective, allowing studies to invest in larger, more robust sampling designs. As such, deep learning will be a game changer for ecological research helping to improve the quality and quantity of the data that can be collected. Within this chapter we introduce two case studies to demonstrate the application of deep learning techniques in marine ecological studies. The first example demonstrates the use of deep learning in the detection and classification of an important underwater ecosystem in the Mediterranean (Posidonia oceanica seagrass meadows), the other showcases the automatic identification of several jellyfish species in coastal areas. Both applications showed high levels of accuracy in the detection and identification of the study organisms, which represents encouraging results for the applicability of these methodologies in marine ecological studies. Despite its potential, deep learning has yet not been widely adopted in ecological studies. Information technologists and natural scientists alike need to more actively collaborate to move forward in this field of science. Cost-effective data collection solutions are desperately needed in a time when large amounts of data are required to detect and adapt to global environmental change.

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Acknowledgements

This work is partially supported by Ministry of Economy and Competitiveness (AEI, FEDER, UE), under contracts TIN2017-85572-P, DPI2017-86372-C3-1-R. H Hinz was supported by the Ramón y Cajal Fellowship (grant by the Ministerio de Economía y Competitividad de España and the Conselleria d’Educació, Cultura i Universitats Comunidad Autonoma de las Islas Baleares). We would like to thank Charlotte Jennings for her help in identifying and labelling jellyfish in underwater images.

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Authors and Affiliations

  1. Departament de Ciències Matemàtiques i Informàtica, Systems Robotics and Vision Group, Universitat de les Illes Balears, Ctra. Valldemossa Km 7.5, 07122, Palma, Spain

    Miguel Martin-Abadal & Yolanda Gonzalez-Cid

  2. Department of Marine Ecosystem Dynamics, Esporles (Illes Balears), IMEDEA (CSIC-UIB), Institut Mediterranid’Estudis Avançats, Miquel Marquès 21, 07190, Esporles, Spain

    Ana Ruiz-Frau & Hilmar Hinz

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  1. Miguel Martin-Abadal
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  2. Ana Ruiz-Frau
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Correspondence to Miguel Martin-Abadal .

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Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  2. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

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Martin-Abadal, M., Ruiz-Frau, A., Hinz, H., Gonzalez-Cid, Y. (2020). The Application of Deep Learning in Marine Sciences. In: Pedrycz, W., Chen, SM. (eds) Deep Learning: Algorithms and Applications. Studies in Computational Intelligence, vol 865. Springer, Cham. https://doi.org/10.1007/978-3-030-31760-7_7

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