ArticleScientific reports2022
FathomNet: A global image database for enabling artificial intelligence in the ocean.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Deep blueprint: A literature review and guide to automated image classification for ecologists.The Journal of animal ecology · 2026Review
- Leveraging artificial intelligence to enhance marine biosecurity.Bioscience · 2026Review
- New features of Peristediidae fishes in the South China Sea.Journal of fish biology · 2026Article
- Tilapia Feeding Behavior Image Dataset: A Benchmark Resource for Automated Feeding Intensity Recognition in Aquaculture.Scientific data · 2026Article
- Improving rare-class detection in deep-sea imagery via generative augmentation with stable diffusion.Scientific reports · 2026Article
- The Global Deep Sea Exploration Goals: A representative approach to visually observing the deep seafloor.Science advances · 2026Article
- Analysis of Biological Images and Quantitative Monitoring Using Deep Learning and Computer Vision.Journal of imaging · 2026Review
- Collective Science to Inform Global Ocean Protections.Ecology letters · 2025Article
- BenthicNet: A global compilation of seafloor images for deep learning applications.Scientific data · 2025Article
- Article
- Artificial intelligence for geoscience: Progress, challenges, and perspectives.Innovation (Cambridge (Mass.)) · 2024Review
- An in situ digital synthesis strategy for the discovery and description of ocean life.Science advances · 2024Article
- An interactive AI-driven platform for fish age reading.PloS one · 2024Article
- Article
- A synergistic future for AI and ecology.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Image dataset for benchmarking automated fish detection and classification algorithms.Scientific data · 2023Article
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Authors and funding
11 authors.
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Abstract
The ocean is experiencing unprecedented rapid change, and visually monitoring marine biota at the spatiotemporal scales needed for responsible stewardship is a formidable task. As baselines are sought by the research community, the volume and rate of this required data collection rapidly outpaces our abilities to process and analyze them. Recent advances in machine learning enables fast, sophisticated analysis of visual data, but have had limited success in the ocean due to lack of data standardization, insufficient formatting, and demand for large, labeled datasets. To address this need, we built FathomNet, an open-source image database that standardizes and aggregates expertly curated labeled data. FathomNet has been seeded with existing iconic and non-iconic imagery of marine animals, underwater equipment, debris, and other concepts, and allows for future contributions from distributed data sources. We demonstrate how FathomNet data can be used to train and deploy models on other institutional video to reduce annotation effort, and enable automated tracking of underwater concepts when integrated with robotic vehicles. As FathomNet continues to grow and incorporate more labeled data from the community, we can accelerate the processing of visual data to achieve a healthy and sustainable global ocean.
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