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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)

Published by NOAA National Centers for Environmental Information | National Oceanic and Atmospheric Administration, Department of Commerce | Catalog Last Checked: August 03, 2026 at 05:28 AM | Dataset Last Updated: July 22, 2026 at 12:00 AM
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.

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0316847 LATITUDE LONGITUDE SALINITY water depth WATER TEMPERATURE camera CTD

Complete Metadata
@type dcat:Dataset
accessLevel non-public
contactPoint 
{
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  "@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 
[
  {
    "@type": "dcat:Distribution",
    "title": "NCEI Dataset Landing Page",
    "accessURL": "https://www.ncei.noaa.gov/archive/accession/0316847",
    "mediaType": "text/html",
    "description": "Navigate directly to the URL for a descriptive web page with download links.",
    "describedByType": "application/octet-stream"
  },
  {
    "@type": "dcat:Distribution",
    "title": "Descriptive Information",
    "accessURL": "https://www.ncei.noaa.gov/archive/accession/oas/316847",
    "mediaType": "text/html",
    "description": "Navigate directly to the URL for a descriptive web page.",
    "describedByType": "application/octet-stream"
  },
  {
    "@type": "dcat:Distribution",
    "title": "HTTPS",
    "accessURL": "https://www.ncei.noaa.gov/archive/accession/download/316847",
    "mediaType": "text/html",
    "description": "Navigate directly to the URL for a descriptive web page.",
    "describedByType": "application/octet-stream"
  },
  {
    "@type": "dcat:Distribution",
    "title": "NOAA National Centers for Environmental Information",
    "accessURL": "https://www.ncei.noaa.gov/contact",
    "mediaType": "text/html",
    "description": "Users may contact NCEI for more information and to request access to the archived data. User requests must be approved by NCEI and are subject to order fulfillment constraints.",
    "describedByType": "application/octet-stream"
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  {
    "@type": "dcat:Distribution",
    "title": "GCMD Keyword Forum Page",
    "accessURL": "https://forum.earthdata.nasa.gov/app.php/tag/GCMD%2BKeywords",
    "mediaType": "text/html",
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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)

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