---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    sequence: int64
  splits:
  - name: train
    num_bytes: 51027416699.38
    num_examples: 8716
  - name: validation
    num_bytes: 14065562052.042
    num_examples: 2886
  - name: test
    num_bytes: 13973797429.51
    num_examples: 2890
  download_size: 35506863059
  dataset_size: 79066776180.93199
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
task_categories:
- multilabel-image-classification
tags:
- marine biology
- biodiversity
- ecology
- conservation
- citizen science
- coral reef
license: cc0-1.0
size_categories:
- 10K<n<100K
---

# Seatizen Atlas Image Dataset

---

## Dataset Card

**Dataset Name:** Seatizen Atlas Image Dataset  
**Task:** Multi-label image classification  
**Domain:** Marine Biodiversity  
**License:** cc0-1.0  
**Size:** 14,492 annotated images

---

## Description

The **Seatizen Atlas Image Dataset** is a large-scale collection of annotated underwater images designed for training and evaluating artificial intelligence models in marine biodiversity research. It is specifically tailored for multi-label image classification tasks.

This dataset was curated by selecting only classes with more than 200 annotations to facilitate the training of computer vision models. For more information about the Seatizen Atlas Dataset, please refer to the [Data paper](https://www.nature.com/articles/s41597-024-04267-z#Sec20) and the [Zenodo repository](https://zenodo.org/records/13951614).


## Acquisition Details

Underwater data acquisition was carried out using instrumented marine platforms. These platforms can be divided into two groups: citizen platforms and scientific platforms.

- **Citizen platforms** paddleboards, kitesurfs, snorkeling masks equipped with cameras. Images were collected by the "Seatizen" team to promote citizen science involvement in marine research.
- **Scientific platforms** Autonomous Surface Vehicles (ASV) equipped with a camera.

## Dataset Structure

The Seatizen Atlas Image Dataset is organized as follows:

- **Train split:** 8716 images
- **Validation split:** 2886 images
- **Test split:** 2890 images

![Label Distribution](label_distribution_seatizen_atlas.png)


---

## List of Annotated Classes

31 classes were grouped into five main categories:

### Algae
- Algal Assemblage
- Algae Halimeda
- Algae Coralline
- Algae Turf

### Coral
- Acropora Branching
- Acropora Digitate
- Acropora Submassive
- Acropora Tabular
- Bleached Coral
- Dead Coral
- Living Coral
- Non-acropora Millepora
- 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 and Custom Classes
- Atra/Leucospilota
- Blurred
- Fish
- Homo Sapiens
- Human Object
- Sea Cucumber
- Sea Urchin
- Sponges
- Useless

More detailed information about the dataset classes, including full descriptions and examples of annotated images, can be found in the supplementary material [available here](https://static-content.springer.com/esm/art%3A10.1038%2Fs41597-024-04267-z/MediaObjects/41597_2024_4267_MOESM1_ESM.pdf).


---

## Citation

If you use the Seatizen Atlas Image Dataset in your research, please consider cite the following:

```bibtex
@article{Contini2025,
   author = {Matteo Contini and Victor Illien and Mohan Julien and Mervyn Ravitchandirane and Victor Russias and Arthur Lazennec and Thomas Chevrier and Cam Ly Rintz and Léanne Carpentier and Pierre Gogendeau and César Leblanc and Serge Bernard and Alexandre Boyer and Justine Talpaert Daudon and Sylvain Poulain and Julien Barde and Alexis Joly and Sylvain Bonhommeau},
   doi = {10.1038/s41597-024-04267-z},
   issn = {2052-4463},
   issue = {1},
   journal = {Scientific Data},
   pages = {67},
   title = {Seatizen Atlas: a collaborative dataset of underwater and aerial marine imagery},
   volume = {12},
   url = {https://doi.org/10.1038/s41597-024-04267-z},
   year = {2025},
}