TY - JOUR
T1 - Low-Cost, Deep-Sea Imaging and Analysis Tools for Deep-Sea Exploration
T2 - A Collaborative Design Study
AU - Bell, Katherine L.C.
AU - Chow, Jennifer Szlosek
AU - Hope, Alexis
AU - Quinzin, Maud C.
AU - Cantner, Kat A.
AU - Amon, Diva J.
AU - Cramp, Jessica E.
AU - Rotjan, Randi D.
AU - Kamalu, Lehua
AU - de Vos, Asha
AU - Talma, Sheena
AU - Buglass, Salome
AU - Wade, Veta
AU - Filander, Zoleka
AU - Noyes, Kaitlin
AU - Lynch, Miriam
AU - Knight, Ashley
AU - Lourenço, Nuno
AU - Girguis, Peter R.
AU - de Sousa, João Borges
AU - Blake, Chris
AU - Kennedy, Brian R.C.
AU - Noyes, Timothy J.
AU - McClain, Craig R.
N1 - Funding Information:
We thank one anonymous interviewee for their participation and thoughtful contributions to this study. We thank the Maka Niu Concept Development Team: Nainoa Thompson, Daniel Novy, Allan Adams, Sonja Swenson Rogers, and Noelani Kamalu; the Maka Niu Engineering Team: Daniel Novy, Lui Kawasumi, Allan Adams, Kat Cantner, Jon Ferguson, Margaret Sullivan, and Peter Bell; and, additional Maka Niu test users who worked with the co-authors: Osei Agyapong, João Andrade, Corie Boolukos, João Costa, Noelani Kamalu, Andreas Ratteray, Caroline Schio. Thanks to the E/V Nautilus and Mesobot teams for deploying a Maka Niu unit on cruise NA131. Thank you to the Ecole Flottant organizers and funders, especially IFREMER, IRD, WIOMSA, and University of Reunion Island, for the successful deployment of the Maka Niu on the towed camera, SCAMPI. A special thank you to Simon Tranvouez and Renaud Quinquis of Genavir for their support, advice, and enthusiasm during deployments of the Maka Niu. We also acknowledge the FathomNet team, particularly Kakani Katija and Benjamin Woodward. Finally, sincere thanks Susan Poulton and Jessica Sandoval, as well as reviewers Ana Hilário and David A Bowden, who provided thoughtful and challenging reviews that greatly improved this manuscript.
Funding Information:
The participatory design study was funded by the MIT Media Lab Open Ocean Initiative, MIT Portugal Program, Oceankind, and National Philanthropic Trust. The development of Maka Niu was funded by the MIT Portugal Program, MIT Media Lab Open Ocean Initiative, MIT Future Ocean Lab, and Oceanic Labs. User testing and deployments are supported in part by the National Philanthropic Trust. Funding for RR and BK was provided by the National Geographic Society's grant for My Deep Sea, My Backyard (NGSBU-PFA-2018-03). Support for AV was provided by the Schmidt Foundation. Participation of numerous co-authors was supported by their respective organizations. Seed funding for FathomNet was provided by the National Geographic Society (#518018), National Oceanic and Atmospheric Administration (NA18OAR4170105), and the Monterey Bay Aquarium Research Institute through support from the David and Lucile Packard Foundation. Additional funding for AI-enabled video analysis has been provided by the National Geographic Society (NGS-86951T-21) and the National Science Foundation (OTIC #1812535 & Convergence Accelerator #2137977). The Ocean Discovery League supported open-access publication fees.
Publisher Copyright:
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.
PY - 2022/8/11
Y1 - 2022/8/11
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - capacity development
KW - co-design
KW - machine learning
KW - marine science
KW - ocean exploration
KW - participatory design
KW - technology
UR - http://www.scopus.com/inward/record.url?scp=85137755182&partnerID=8YFLogxK
U2 - 10.3389/fmars.2022.873700
DO - 10.3389/fmars.2022.873700
M3 - Article
AN - SCOPUS:85137755182
SN - 2296-7745
VL - 9
JO - Frontiers in Marine Science
JF - Frontiers in Marine Science
M1 - 873700
ER -