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Related Experiment Video

Updated: Feb 4, 2026

Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information
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Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information

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Deep-sea image dataset for organism detection.

Takaki Nishio1, Yuki Kawae1

  • 1Japan Agency for Marine-Earth Science and Technology (JAMSTEC), 3173-25 Showa-machi, Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan.

Data in Brief
|February 2, 2026
PubMed
Summary

A new dataset, JODD, aids deep-sea organism detection. This resource supports developing automated methods for marine biodiversity research and conservation efforts.

Keywords:
Artificial intelligenceBenthosDeep learningHOVROVSeafloor

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Area of Science:

  • Marine Biology
  • Computer Science
  • Data Science

Background:

  • Effective marine resource conservation and pollution mitigation depend on comprehensive deep-sea biodiversity knowledge.
  • Manual analysis of deep-sea imagery for organism distribution is time-consuming and inconsistent, necessitating automated solutions.

Purpose of the Study:

  • To introduce the J-EDI Organism Detection Dataset (JODD), a novel dataset designed for training and evaluating machine learning models for automatic deep-sea organism identification.
  • To provide a standardized resource for advancing automated methods in deep-sea ecological research.

Main Methods:

  • Compilation of 8,151 images with 15,621 bounding boxes, annotated in the Common Objects in Context (COCO) format.
  • Inclusion of images captured via remotely operated vehicles (ROVs) and human-occupied vehicles (HOVs) during Japan Agency for Marine-Earth Science and Technology (JAMSTEC) deep-sea surveys (1984-2021).
  • Dataset comprises 20 categories: 19 biological groups and 1 machine category, sourced from JAMSTEC's publicly available E-library of Deep-sea Images (J-EDI).

Main Results:

  • The JODD dataset offers a substantial and diverse collection of annotated deep-sea imagery.
  • The dataset is structured for direct application in developing and benchmarking deep learning models for object detection.

Conclusions:

  • The JODD dataset represents a significant contribution to the field of deep-sea ecology and AI-driven biodiversity assessment.
  • This resource will accelerate the development of automated tools crucial for marine conservation and research.