
Seatizen Atlas image dataset 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 collaborative dataset of underwater and aerial marine imagery. Download the Dataset 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. Scientific Publication 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. Overview 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). Annotation Details Annotation Types: Multilabel Convention: Identifies all observed classes in an image. Instance Segmentation: Highlights contours of each instance for each class. List of Classes Algae Algal Assemblage Algae Halimeda Algae Coralline Algae Turf Coral Acropora Branching Acropora Digitate Acropora Submassive Acropora Tabular Bleached Coral Dead Coral Gorgonian Living Coral Non-acropora Millepora Non-acropora Branching Non-acropora Encrusting Non-acropora Foliose Non-acropora Massive Non-acropora Coral Free Non-acropora Submassive Seagrass Syringodium Isoetifolium Thalassodendron Ciliatum Habitat Rock Rubble Sand Other Organisms Thorny Starfish Sea Anemone Ascidians Giant Clam Fish Other Starfish Sea Cucumber Sea Urchin Sponges Turtle Custom Classes Blurred Homo Sapiens Human Object Trample Useless Waste 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. Usage Notes 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. Code Availability All related codes for data processing, downloading, and AI model training can be found in the following GitHub repositories: Plancha Workflow Zenodo Tools DinoVdeau Model Acknowledgements 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.
Ecology, Mapping, Artificial Intelligence, FOS: Biological sciences, Global Coral Reef Monitoring Network, Reef Ecosystem, Citizen Sciences, Coral Reef, Indian Ocean, Coral Reef Habitat
Ecology, Mapping, Artificial Intelligence, FOS: Biological sciences, Global Coral Reef Monitoring Network, Reef Ecosystem, Citizen Sciences, Coral Reef, Indian Ocean, Coral Reef Habitat
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