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FathomNet: A global image database for enabling artificial intelligence in the ocean.

Kakani Katija1,2,3, Eric Orenstein4, Brian Schlining4

  • 1Monterey Bay Aquarium Research Institute, Research and Development, Moss Landing, 95039, USA. kakani@mbari.org.

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Summary

FathomNet is an open-source marine image database addressing the challenge of analyzing vast visual data for ocean stewardship. It enables faster processing and automated tracking of underwater concepts, crucial for understanding rapid ocean changes.

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Area of Science:

  • Marine biology
  • Oceanography
  • Computer science

Background:

  • The ocean faces rapid environmental changes, necessitating effective visual monitoring of marine life.
  • Current data collection and analysis capabilities struggle to keep pace with the required spatiotemporal scales for ocean stewardship.
  • Machine learning for visual data analysis has been limited in marine environments due to data standardization and labeling challenges.

Purpose of the Study:

  • To develop an open-source image database, FathomNet, for standardizing and aggregating curated, labeled marine visual data.
  • To facilitate the training and deployment of machine learning models for analyzing underwater imagery.
  • To enable automated tracking of underwater objects and concepts, supporting ocean research and conservation.

Main Methods:

  • Creation of FathomNet, an open-source database, to standardize and aggregate labeled marine imagery.
  • Seeding FathomNet with diverse imagery including marine animals, equipment, and debris.
  • Demonstration of using FathomNet data to train machine learning models and reduce annotation efforts.
  • Integration of FathomNet with robotic vehicles for automated tracking.

Main Results:

  • FathomNet provides a standardized, curated dataset for marine visual data analysis.
  • Trained models using FathomNet data can be deployed on other video datasets, reducing manual annotation needs.
  • Automated tracking of underwater concepts is achievable when FathomNet is integrated with robotic systems.

Conclusions:

  • FathomNet accelerates the processing of visual data for ocean research and stewardship.
  • The database supports the development of machine learning applications for marine environments.
  • Community contributions to FathomNet will enhance its utility for achieving a healthy and sustainable global ocean.