AUTHOR=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. , McClain Craig R. 

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

JOURNAL=Frontiers in Marine Science

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2022.873700

DOI=10.3389/fmars.2022.873700

ISSN=2296-7745

ABSTRACT=A miniscule fraction of the deep sea has been scientifically explored and characterized, due to a number of constraints, including expense, inefficiency, exclusion, and resulting inequitable access to tools and resources around the world. To meet the demand of understanding the most important 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 participatory design study on technical and human 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, as well as artificial intelligence-driven data analysis. It was conducted in partnership with twenty marine professionals from around the world, 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, as well as 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 data analysis platform called Ocean Vision AI is in development. We also identified six categories of open design questions 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 deep-sea participatory design projects and outline our future direction in this space.