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

NSF Convergence Accelerator Track E: Ocean Vision AI: Scaling up visual observations of life in the ocean using artificial intelligence
NSF 融合加速器轨道 E:海洋视觉 AI:利用人工智能扩大对海洋生命的视觉观察
批准号:
2230776
负责人:
Kakani Young
金额:
$499.99万
依托单位国家:
美国
项目类别:
Cooperative Agreement
财政年份:
2022
资助国家:
美国
项目状态:
已结题
起止时间:
2022-09-15 至 2024-08-31

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中文摘要
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英文摘要
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.
期刊论文(2)
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会议论文
DOI: 10.1145/3544548.3580886
发表时间: 2023-03
期刊: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
影响因子: --
作者: [A. Crosby;E. Orenstein;Susan E. Poulton;K. L. Bell;Benjamin Woodward;H. Ruhl;K. Katija;A. Forbes]
通讯作者: A. Crosby;E. Orenstein;Susan E. Poulton;K. L. Bell;Benjamin Woodward;H. Ruhl;K. Katija;A. Forbes
NSF Convergence Accelerator Track E: Ocean Vision AI: Scaling up Visual Observations of Life in the Ocean Using Artificial Intelligence
Collaborative Research: Functional design of siphonophore propulsion and behavior
EAGER - Integrating machine learning on autonomous platforms for target-tracking operations using stereo imagery
Collaborative Research: Mesobot: a robot for investigating the ocean interior
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