Award Abstract # 2230776
NSF Convergence Accelerator Track E: Ocean Vision AI: Scaling up visual observations of life in the ocean using artificial intelligence

NSF Org: ITE
Innovation and Technology Ecosystems
Recipient: MONTEREY BAY AQUARIUM RESEARCH INSTITUTE
Initial Amendment Date: September 15, 2022
Latest Amendment Date: July 27, 2023
Award Number: 2230776
Award Instrument: Cooperative Agreement
Program Manager: Aurali Dade
adade@nsf.gov
 (703)292-7468
ITE
 Innovation and Technology Ecosystems
TIP
 Dir for Tech, Innovation, & Partnerships
Start Date: September 15, 2022
End Date: August 31, 2024 (Estimated)
Total Intended Award Amount: $4,999,918.00
Total Awarded Amount to Date: $4,999,918.00
Funds Obligated to Date: FY 2022 = $2,733,169.00
FY 2023 = $2,266,749.00
History of Investigator:
  • Kakani Young (Principal Investigator)
    kakani@mbari.org
  • Angus Forbes (Co-Principal Investigator)
  • Henry Ruhl (Co-Principal Investigator)
  • Benjamin Woodward (Co-Principal Investigator)
  • Katherine Bell (Co-Principal Investigator)
Recipient Sponsored Research Office: Monterey Bay Aquarium Research Institute
7700 SANDHOLDT RD
MOSS LANDING
CA  US  95039-9644
(831)775-1803
Sponsor Congressional District: 19
Primary Place of Performance: Monterey Bay Aquarium Research Institute
7700 SANDHOLDT RD
MOSS LANDING
CA  US  95039-9644
Primary Place of Performance
Congressional District:
19
Unique Entity Identifier (UEI): GM6EL1UH2L83
Parent UEI:
NSF Program(s): Convergence Accelerator Resrch
Primary Program Source: 01002324DB NSF RESEARCH & RELATED ACTIVIT
01002223DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):
Program Element Code(s): 131Y00
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.084

ABSTRACT

In order to fully explore our ocean and discover the life that lives there, we need to scale up our observational capacity. To address this need, underwater imagery is being collected at rates that far exceed our ability to process them, and new techniques using artificial intelligence are critical. This project, Ocean Vision AI, will accelerate processing of underwater imagery by combining expertise in imaging, artificial intelligence, and open data, and creating data and analysis pipelines that convert pixels to actionable data. Ocean Vision AI will provide opportunities to diversify an ocean data science workforce and public engagement through community science portals and game-based education initiatives. Together, Ocean Vision AI will be used to directly accelerate the automated analysis of underwater visual data to enable scientists, explorers, policymakers, storytellers, and the public, to learn, understand, and care more about the life that inhabits our ocean.

In order to fully explore our ocean and discover the life that lives there, we need to scale up our observational capabilities both in time and space. To address this need, underwater imaging, a major sensing modality for marine biology, is being deployed on a diverse array of platforms. However, as more visual data are collected, the community faces a data analysis backlog that artificial intelligence may be able to address. Ocean Vision AI seeks to address this need by providing a central hub for groups conducting research that use imaging, AI, and open data; create data pipelines from existing image and video data repositories; provide project tools for coordination; leverage public participation and engagement via game development; and generate data products that are shared with researchers as well as other open data repositories. These efforts will result in novel intellectual pursuits in fields as diverse as marine biology, fisheries, biological oceanography, underwater optics and computer vision, artificial intelligence, ocean engineering, biomechanics, environmental biology, human-computer interaction, game-based education, and community contributions to science.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Crosby, Alison and Orenstein, Eric Coughlin and Poulton, Susan E and Bell, Katherine L.C. and Woodward, Benjamin and Ruhl, Henry and Katija, Kakani and Forbes, Angus G. "Designing Ocean Vision AI: An Investigation of Community Needs for Imaging-based Ocean Conservation" Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems , 2023 https://doi.org/10.1145/3544548.3580886 Citation Details
Katija, Kakani and Orenstein, Eric and Schlining, Brian and Lundsten, Lonny and Barnard, Kevin and Sainz, Giovanna and Boulais, Oceane and Cromwell, Megan and Butler, Erin and Woodward, Benjamin and Bell, Katherine L. "FathomNet: A global image database for enabling artificial intelligence in the ocean" Scientific Reports , v.12 , 2022 https://doi.org/10.1038/s41598-022-19939-2 Citation Details

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