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: Monterey Bay Aquarium Research Institute
• 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
• 起止时间: 2022-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2230776&HistoricalAwards=false
• 关键词: NSF; Convergence; Accelerator; Track; Ocean

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.

为了充分探索我们的海洋并发现生活在那里的生命，我们需要扩大我们的观测能力。为了满足这一需求，水下图像的收集速度远远超过了我们处理它们的能力，使用人工智能的新技术至关重要。这个名为Ocean Vision AI的项目将通过结合成像、人工智能和开放数据方面的专业知识，并创建将像素转换为可操作数据的数据和分析管道，来加速水下图像的处理。Ocean Vision AI将提供机会，通过社区科学门户网站和基于游戏的教育计划，使海洋数据科学劳动力和公众参与多样化。Ocean Vision AI将用于直接加速水下视觉数据的自动化分析，使科学家、探险家、政策制定者、讲故事的人和公众能够更多地了解、理解和关心栖息在我们海洋中的生命。为了充分探索我们的海洋并发现生活在那里的生命，我们需要在时间和空间上扩大我们的观测能力。为了满足这一需要，水下成像，一个主要的海洋生物传感模式，正在部署在各种各样的平台。然而，随着更多的视觉数据被收集，社区面临着人工智能可能能够解决的数据分析积压。Ocean Vision AI旨在通过为使用成像，人工智能和开放数据进行研究的团体提供中心枢纽来满足这一需求;从现有的图像和视频数据存储库创建数据管道;提供项目协调工具;通过游戏开发利用公众参与和参与;并生成与研究人员以及其他开放数据存储库共享的数据产品。这些努力将在海洋生物学、渔业、生物海洋学、水下光学和计算机视觉、人工智能、海洋工程、生物力学、环境生物学、人机交互、基于游戏的教育、该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准。

项目成果

Designing Ocean Vision AI: An Investigation of Community Needs for Imaging-based Ocean Conservation

• 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
• 作者: A. Crosby;E. Orenstein;Susan E. Poulton;K. L. Bell;Benjamin Woodward;H. Ruhl;K. Katija;A. Forbes