Underwater images collected by an Autonomous Surface Vehicle in St-Leu, Réunion - 2025-04-25

This dataset was collected by an Autonomous Surface Vehicle in St-Leu, Réunion - 2025-04-25. Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps. This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. Methods, tools and scientific objectives are also described in a dedicated data paper. Generic folder structure YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number ├── DCIM : folder to store videos and photos depending on the media collected. ├── GPS : folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. │ ├── BASE : files coming from rtk station or any static positioning instrument. │ └── DEVICE : files coming from the device. ├── METADATA : folder with general information files about the session. ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. │ ├── BATHY : output folder for bathymetry raw data extracted from mission logs. │ ├── FRAMES : output folder for georeferenced frames extracted from DCIM videos. │ ├── IA : destination folder for image recognition predictions. │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot, mission plan etc.). Software All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!本数据集由自主水面航行器（Autonomous Surface Vehicle）于2025年4月25日在留尼旺圣勒（St-Leu, Réunion）采集。 科学家或公众采集的水下与航拍图像可广泛应用于科学研究、资源管理与生态保护领域。此类图像经标注后可用于训练人工智能模型，以实现图像内目标物的自动识别。我们提供一套涵盖硬件与软件的工具集，用于海洋数据采集、物种或生境识别以及地图生成。 本数据集隶属于涵盖大量水下与航拍图像的大型数据集集合Seatizen Altas。相关方法、工具集与科学目标已在专属数据论文中详细阐述。 通用文件夹结构 YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number ├── DCIM：用于存储采集到的各类媒体文件（视频与照片）的目录。 ├── GPS：用于存储各类定位相关文件的目录。若可对文件进行定位校正（例如基于RINEX数据进行后处理运动学校正），则需将基准站数据与设备采集数据分别存放；若仅存在设备定位数据且无法通过后处理技术校正（例如GPX格式文件），则无需区分基准站与设备数据，文件直接存放于GPS目录根目录下。 │ ├── BASE：存放来自RTK（Real-Time Kinematic）基站或其他静态定位仪器的文件。 │ └── DEVICE：存放来自采集设备的文件。 ├── METADATA：存储本次任务通用信息文件的目录。 ├── PROCESSED_DATA：用于存储本次任务数据处理结果的各级目录。 │ ├── BATHY：用于存储从任务日志中提取的测深原始数据的输出目录。 │ ├── FRAMES：用于存储从DCIM目录下视频中提取的地理参考帧的输出目录。 │ ├── IA：用于存储图像识别预测结果的目标目录。 │ └── PHOTOGRAMMETRY：用于存储摄影测量重建模型的目标目录。 └── SENSORS：用于存储其他来源文件的目录（例如回声测深仪采集的测深数据、自动驾驶仪日志文件、任务规划文件等）。 软件 所有原始数据均通过我们的工作流进行处理，所有预测结果均由我们的推理流水线生成。您可在本代码仓库中获取下载该数据集所需的全部脚本。祝您使用SeatizenDOI开展研究顺利！