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

arXiv: https://arxiv.org/abs/2303.05480 · HTML: https://mlsanity.com/paper/2303.05480/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2023.

## Built on by (most cited first, 3 of 3)

- [Generative AI Uses and Risks for Knowledge Workers in a Science Organization](https://mlsanity.com/paper/2501.16577/) (2025)
- [FathomGPT: A Natural Language Interface for Interactively Exploring Ocean Science Data](https://mlsanity.com/paper/2412.02784/) (2024)
- [FathomVerse: A community science dataset for ocean animal discovery](https://mlsanity.com/paper/2412.01701/) (2024)

## Abstract (arXiv)

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.