FathomNet: An open-source image database for training, testing, and validating machine learning algorithms to help us understand our ocean and its inhabitants (NCEI Accession 0316847)
FathomNet is an open-source image database that standardizes and aggregates expertly curated, labeled data for training, testing, and validating state-of-the-art artificial intelligence algorithms. It's been seeded with existing iconic and non-iconic imagery of marine animals, equipment, debris, and other concepts, and is built to accept future contributions from distributed data sources.
This addresses a critical need: the ocean is experiencing unprecedented rapid change, and visually monitoring marine biota at the spatiotemporal scales needed for responsible stewardship is a formidable task. Recent advances in machine learning enable fast, sophisticated analysis of visual data, but have had limited success in the ocean due to a lack of data standardization, insufficient formatting, and the demand for large, labeled datasets.
Resources
5 resources available
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NCEI Dataset Landing Page
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Descriptive Information
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HTTPS
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NOAA National Centers for Environmental Information
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GCMD Keyword Forum Page
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | non-public |
| contactPoint |
{ "fn": "NOAA National Centers for Environmental Information", "@type": "vcard:Contact", "hasEmail": "mailto:ncei.info@noaa.gov" } |
| describedByType | application/octet-steam |
| description | FathomNet is an open-source image database that standardizes and aggregates expertly curated, labeled data for training, testing, and validating state-of-the-art artificial intelligence algorithms. It's been seeded with existing iconic and non-iconic imagery of marine animals, equipment, debris, and other concepts, and is built to accept future contributions from distributed data sources. This addresses a critical need: the ocean is experiencing unprecedented rapid change, and visually monitoring marine biota at the spatiotemporal scales needed for responsible stewardship is a formidable task. Recent advances in machine learning enable fast, sophisticated analysis of visual data, but have had limited success in the ocean due to a lack of data standardization, insufficient formatting, and the demand for large, labeled datasets. |
| distribution |
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| identifier | https://data.noaa.gov/waf/NOAA/NESDIS/ncei/accessions/iso/xml/0316847.xml |
| issued | 2026-07-22T00:00:00.000+00:00 |
| keyword |
[ "0316847", "LATITUDE", "LONGITUDE", "SALINITY", "water depth", "WATER TEMPERATURE", "camera", "CTD", "GPS", "pressure sensor", "temperature sensor", "in situ", "Autonomous Underwater Vehicle", "Monterey Bay Aquarium Research Institute", "Monterey Bay Aquarium Research Institute", "East Siberian Sea", "South Pacific Ocean", "oceanography", "MBARI > Monterey Bay Aquarium Research Institute", "FathomNet", "FathomVerse", "NOAA Fisheries", "US DOC; NOAA; OAR; Office of Ocean Exploration and Research (OER)", "EARTH SCIENCE > OCEANS > BATHYMETRY/SEAFLOOR TOPOGRAPHY > WATER DEPTH", "EARTH SCIENCE > OCEANS > OCEAN TEMPERATURE > WATER TEMPERATURE", "EARTH SCIENCE > OCEANS > SALINITY/DENSITY > SALINITY", "Image Coordinates", "Marine Species", "Still Image", "CAMERA > CAMERA", "CTD > Conductivity, Temperature, Depth", "GPS RECEIVERS > GPS RECEIVERS", "PRESSURE SENSORS > PRESSURE SENSORS", "TEMPERATURE SENSORS > TEMPERATURE SENSORS", "human or ML Model", "Drop Camera", "Remotely Operated Vehicle", "SCUBA", "OCEAN > ARCTIC OCEAN", "OCEAN > PACIFIC OCEAN > SOUTH PACIFIC OCEAN", "global ocean", "4YKL7R" ] |
| landingPage | https://www.ncei.noaa.gov/contact |
| language |
[]
|
| license | https://creativecommons.org/publicdomain/zero/1.0/ |
| modified | 2026-07-22T00:00:00.000+00:00 |
| publisher |
{ "name": "NOAA National Centers for Environmental Information", "@type": "org:Organization" } |
| rights | otherRestrictions |
| spatial | 178.224465,-29.92328,-179.997149,71.006527 |
| temporal | 1989-04-27T00:00:00+00:00/2026-05-05T00:00:00+00:00 |
| theme |
[ "geospatial" ] |
| title | FathomNet: An open-source image database for training, testing, and validating machine learning algorithms to help us understand our ocean and its inhabitants (NCEI Accession 0316847) |
