Designing Ocean Vision AI: An Investigation of Community Needs for Imaging-based Ocean Conservation
Designing Ocean Vision AI: An Investigation of Community Needs for Imaging-based Ocean Conservation
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DOI：
10.1145/3544548.3580886
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发表时间：
2023-03
期刊：
影响因子：
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通讯作者：
A. Crosby;E. Orenstein;Susan E. Poulton;K. L. Bell;Benjamin Woodward;H. Ruhl;K. Katija;A. Forbes
中科院分区：
文献类型：
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作者：
A. Crosby;E. Orenstein;Susan E. Poulton;K. L. Bell;Benjamin Woodward;H. Ruhl;K. Katija;A. Forbes
研究各种生物和现象的海洋科学家越来越依赖成像设备进行研究。这些科学家有许多工具来收集数据，但很少有资源用于自动分析。在本文中，我们报告了与不同利益攸关方的讨论，以确定社区需求，并为正在进行的海洋科学特定分析工具的开发制定一套功能要求。我们对在蓝色经济领域工作的个人进行了36次深入访谈，揭示了阻碍海洋科学有效成像分析监测工具开发的四个核心问题。我们还确定了12个将参与这些服务的用户原型。此外，我们还举办了一个研讨会，来自35个国家的246名参与者围绕FathomNet，一个基于网络的开源海洋研究图像数据库。这些讨论的结果被用于定义Ocean Vision AI的功能集和界面设计，Ocean Vision AI是一套工具和服务，旨在提高海洋生物的观测能力。
Ocean scientists studying diverse organisms and phenomena increasingly rely on imaging devices for their research. These scientists have many tools to collect their data, but few resources for automated analysis. In this paper, we report on discussions with diverse stakeholders to identify community needs and develop a set of functional requirements for the ongoing development of ocean science-specific analysis tools. We conducted 36 in-depth interviews with individuals working in the Blue Economy space, revealing four central issues inhibiting the development of effective imaging analysis monitoring tools for marine science. We also identified twelve user archetypes that will engage with these services. Additionally, we held a workshop with 246 participants from 35 countries centered around FathomNet, a web-based open-source annotated image database for marine research. Findings from these discussions are being used to define the feature set and interface design of Ocean Vision AI, a suite of tools and services to advance observational capabilities of life in the ocean.