Designing Ocean Vision AI: An Investigation of Community Needs for Imaging-based Ocean Conservation
Alison Crosby, Eric Coughlin Orenstein, Susan E. Poulton, Katherine L. C. Bell, Benjamin G. Woodward, Henry Ruhl, Kakani Katija, Angus G. Forbes
摘要
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.
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- Generative AI Uses and Risks for Knowledge Workers in a Science OrganizationKelly B. Wagman, Matthew T. Dearing, Marshini ChettyCHI 2025 · 被引用 16 次
- FathomGPT: A natural language interface for interactively exploring ocean science dataNabin Khanal, Chun Meng Yu, Jui-Cheng Chiu, Anav Chaudhary 等UIST 2024 · 被引用 7 次
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- WILDS: A Benchmark of in-the-Wild Distribution ShiftsPang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie 等ICML 2021 · 被引用 1,773 次
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