NSF Convergence Accelerator Track E: Ocean Vision AI: Scaling up Visual Observations of Life in the Ocean Using Artificial Intelligence

OIA-2137977美国国家科学基金会汇聚加速器轨道E：海洋视觉人工智能：利用人工智能扩大对海洋中生命的视觉观测该项目将扩大社会的观测能力，以全面探索海洋并发现生活在那里的动物的全部光谱。海洋代表着地球上最大的宜居生态系统，但只有不到5%的体积被探索，近50%的海洋生物尚未得到描述。为了缩小这一差距，海洋生物的一种主要传感方式--水下成像正被部署在各种平台上。然而，随着更多可视化数据的收集，社区面临着数据分析积压。这个名为海洋视觉AI的项目将部署人工智能(AI)和机器学习，通过自动化水下图像和视频分析来解决这一积压问题。研究活动包括来自研究界以及普通公众、视频游戏玩家、高级高中和社区大学生的贡献。总之，海洋视觉人工智能将被用于直接加速水下视觉数据的自动分析，使科学家、探险家、政策制定者、讲故事者和公众能够更多地学习、理解和关心海洋中的生命。研究团队将吸引众多部门(例如，学术、政府、非营利组织、营利性组织)的研究人员和创新者，以提高社会对海洋生物的观察能力。海洋视觉AI(人工智能)旨在为孵化使用成像、人工智能、开放数据和硬件/软件的研究小组提供中央枢纽；从现有的图像和视频数据存储库创建数据管道；并提供项目协调工具。此外，海洋视觉AI将通过游戏开发利用公众的参与和参与，并将产生与研究人员以及其他美国和全球开放数据库共享的数据产品。这项研究将促进对水下群落、地貌和海洋垃圾的大规模时空调查，并通过使用分类元数据标准向专家广泛提供分类良好的图像，加速海洋生物的发现。此外，该项目将能够通过FathomNet数据库通过新的训练数据来协调和扩大水下计算机视觉研究和机器学习算法的开发。该项目最终将在海洋生物学、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、基于游戏的教育和社区对科学的贡献等领域实现创新的智力追求。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

OIA - 2137977 NSF Convergence Accelerator Track E: Ocean Vision AI: Scaling up visual observations of life in the ocean using artificial intelligenceAbstractThis project will scale up society’s observational capabilities to fully explore the ocean and discover the full spectrum of animals that live there. The ocean represents the largest habitable ecosystem on the planet, yet less than 5% of that volume has been explored, and nearly 50% of marine life are yet to be described. To close this gap, 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. This project, Ocean Vision AI, will deploy artificial intelligence (AI) and machine learning to address this backlog by automating underwater image and video analysis. The research activities include contributions from the research community as well as the general public, video game players, and advanced high school and community college students. 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 the oceans.The research team will engage researchers and innovators across numerous sectors (e.g., academic, government, non-profit, for-profit) to advance society’s observational capabilities of marine life. Ocean Vision AI (artificial intelligence) aims to provide a central hub for incubating groups conducting research that use imaging, AI, open data, and hardware/software; create data pipelines from existing image and video data repositories; and provide project tools for coordination. In addition, Ocean Vision AI will leverage public participation and engagement via game development, and will result in data products that are shared with researchers as well as other US and global open data repositories. This research will facilitate large-scale, spatiotemporal surveys of underwater communities, geomorphology, and marine debris, and accelerate the discovery of marine life by making well classified images widely available to experts using taxonomic metadata standards. Moreover, this project will be able to coordinate and scale underwater computer vision research and machine learning algorithm development via novel training data through the FathomNet database. This project will ultimately enable innovative 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.