
This repository contains the resources and tools for accessing and utilizing the annotated images within the Seatizen Atlas dataset, as described in the paper Seatizen Atlas: a collabora….
This annotated dataset is part of a bigger dataset composed of labeled and unlabeled images. To access information about the whole dataset, please visit the Zenodo repository and follow the download instructions provided.
If you are interested in training AI models using this dataset, you can directly access the processed version on Hugging Face.
This version is already split into training, validation and test sets, and includes only the classes with more than 200 annotations for more robust model training.
An example of a trained model based on this dataset is DinoVdeau.
If you use this dataset in your research, please consider citing the associated paper:
@article{contini2025seatizen,
title={Seatizen Atlas: a collaborative dataset of underwater and aerial marine imagery},
author={Contini, Matteo and Illien, Victor and Julien, Mohan and Ravitchandirane, Mervyn and Russias, Victor and Lazennec, Arthur and Chevrier, Thomas and Rintz, Cam Ly and Carpentier, L{\'e}anne and Gogendeau, Pierre and others},
journal={Scientific Data},
volume={12},
number={1},
pages={67},
year={2025},
publisher={Nature Publishing Group UK London}
}
For detailed information about the dataset and experimental results, please refer to the previous paper.
The Seatizen Atlas dataset includes 14,492 multilabel and 1,200 instance segmentation annotated images. These images are useful for training and evaluating AI models for marine biodiversity research. The annotations follow standards from the Global Coral Reef Monitoring Network (GCRMN).
These classes reflect the biodiversity and variety of habitats captured in the Seatizen Atlas dataset, providing valuable resources for training AI models in marine biodiversity research.
The annotated images are available for non-commercial use. Users are requested to cite the related publication in any resulting works. A GitHub repository has been set up to facilitate data reuse and sharing: GitHub Repository.
All related codes for data processing, downloading, and AI model training can be found in the following GitHub repositories:
This dataset and associated research have been supported by several organizations, including the Seychelles Islands Foundation, Réserve Naturelle Marine de la Réunion, and Monaco Explorations, among others.
For any questions or collaboration inquiries, please contact seatizen.ifremer@gmail.com.