Low-Cost, Deep-Sea Imaging and Analysis Tools for Deep-Sea Exploration: A Collaborative Design Study

Bell, Katherine L.C., Chow, Jennifer Szlosek, Hope, Alexis, Quinzin, Maud C., Cantner, Kat A., Amon, Diva J., Cramp, Jessica E., Rotjan, Randi D., Kamalu, Lehua, de Vos, Asha, Talma, Sheena, Buglass, Salome, Wade, Veta, Filander, Zoleka, Noyes, Kaitlin, Lynch, Miriam, Knight, Ashley, Lourenço, Nuno, Girguis, Peter R., de Sousa, João Borges, Blake, Chris, Kennedy, Brian R.C., Noyes, Timothy J., and McClain, Craig R. (2022) Low-Cost, Deep-Sea Imaging and Analysis Tools for Deep-Sea Exploration: A Collaborative Design Study. Frontiers in Marine Science, 9. 873700.

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Abstract

A minuscule fraction of the deep sea has been scientifically explored and characterized due to several constraints, including expense, inefficiency, exclusion, and the resulting inequitable access to tools and resources around the world. To meet the demand for understanding the largest biosphere on our planet, we must accelerate the pace and broaden the scope of exploration by adding low-cost, scalable tools to the traditional suite of research assets. Exploration strategies should increasingly employ collaborative, inclusive, and innovative research methods to promote inclusion, accessibility, and equity to ocean discovery globally. Here, we present an important step toward this new paradigm: a collaborative design study on technical capacity needs for equitable deep-sea exploration. The study focuses on opportunities and challenges related to low-cost, scalable tools for deep-sea data collection and artificial intelligence-driven data analysis. It was conducted in partnership with twenty marine professionals worldwide, covering a broad representation of geography, demographics, and domain knowledge within the ocean space. The results of the study include a set of technical requirements for low-cost deep-sea imaging and sensing systems and automated image and data analysis systems. As a result of the study, a camera system called Maka Niu was prototyped and is being field-tested by thirteen interviewees and an online AI-driven video analysis platform is in development. We also identified six categories of open design and implementation questions highlighting participant concerns and potential trade-offs that have not yet been addressed within the scope of the current projects but are identified as important considerations for future work. Finally, we offer recommendations for collaborative design projects related to the deep sea and outline our future work in this space.

Item ID: 76448
Item Type: Article (Research - C1)
ISSN: 2296-7745
Keywords: artificial intelligence, capacity development, co-design, machine learning, marine science, ocean exploration, participatory design, technology
Copyright Information: Copyright © 2022 Bell, Chow, Hope, Quinzin, Cantner, Amon, Cramp, Rotjan, Kamalu, de Vos, Talma, Buglass, Wade, Filander, Noyes, Lynch, Knight, Lourenço, Girguis, de Sousa, Blake, Kennedy, Noyes and McClain. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Date Deposited: 21 Mar 2023 06:13
FoR Codes: 37 EARTH SCIENCES > 3708 Oceanography > 370803 Physical oceanography @ 100%
SEO Codes: 18 ENVIRONMENTAL MANAGEMENT > 1805 Marine systems and management > 180599 Marine systems and management not elsewhere classified @ 100%
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