Contini Matteo, Illien Victor, Julien Mohan, Ravitchandirane Mervyn, Russias Victor, Lazennec Arthur, Chevrier Thomas, Rintz Cam Ly, Carpentier Léanne, Gogendeau Pierre, Leblanc César, Bernard Serge, Boyer Alexandre, Talpaert Daudon Justine, Poulain Sylvain, Barde Julien, Joly Alexis, Bonhommeau Sylvain
IFREMER Délégation Océan Indien (DOI), Le Port, 97420, La Réunion, Rue Jean Bertho, France.
INRIA, LIRMM, Université de Montpellier, CNRS, Montpellier, 34000, France.
Sci Data. 2025 Jan 14;12(1):67. doi: 10.1038/s41597-024-04267-z.
Citizen Science initiatives have a worldwide impact on environmental research by providing data at a global scale and high resolution. Mapping marine biodiversity remains a key challenge to which citizen initiatives can contribute. Here we describe a dataset made of both underwater and aerial imagery collected in shallow tropical coastal areas by using various low cost platforms operated either by citizens or researchers. This dataset is regularly updated and contains >1.6 M images from the Southwest Indian Ocean. Most of images are geolocated, and some are annotated with 51 distinct classes (e.g. fauna, and habitats) to train AI models. The quality of these photos taken by action cameras along the trajectories of different platforms, is highly heterogeneous (due to varying speed, depth, turbidity, and perspectives) and well reflects the challenges of underwater image recognition. Data discovery and access rely on DOI assignment while data interoperability and reuse is ensured by complying with widely used community standards. The open-source data workflow is provided to ease contributions from anyone collecting pictures.
公民科学倡议通过在全球范围内提供高分辨率数据，对环境研究产生了全球性影响。绘制海洋生物多样性地图仍然是公民倡议可以做出贡献的关键挑战。在这里，我们描述了一个数据集，该数据集由在热带浅海沿岸地区使用公民或研究人员操作的各种低成本平台收集的水下和航空图像组成。该数据集定期更新，包含来自西南印度洋的超过160万张图像。大多数图像都有地理位置信息，有些还标注了51个不同的类别（如动物群和栖息地），用于训练人工智能模型。这些由行动相机在不同平台轨迹上拍摄的照片质量高度异质（由于速度、深度、浊度和视角的不同），很好地反映了水下图像识别的挑战。数据发现和访问依赖于数字对象标识符（DOI）的分配，而数据的互操作性和重用则通过遵守广泛使用的社区标准来确保。提供了开源数据工作流程，以方便任何收集图片的人做出贡献。
