Underwater images collected by an Autonomous Surface Vehicle in La-Saline, Réunion - 2025-05-15

This dataset was collected by an Autonomous Surface Vehicle in La-Saline, Réunion - 2025-05-15. 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）于留尼汪岛拉萨利讷采集，采集日期为2025年5月15日。 科研人员或普通民众采集的水下与航空影像，可广泛应用于科学研究、资源管理与生态保护领域。此类影像可经标注后共享，用于训练人工智能模型，以实现影像内目标的自动识别。我们提供一套包含硬件与软件的工具集，可用于海洋数据采集、物种或生境预测，以及测绘制图。 本数据集隶属于涵盖海量水下与航空影像的大型合集Seatizen Altas。相关研究方法、工具集及科学目标已在专门的数据论文中详述。 ### 通用文件夹结构 通用文件夹命名格式：`YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number` ├── DCIM：用于存储采集所得各类媒体文件（视频与照片）的目录。 ├── GPS：存储所有与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星历数据的后处理运动学解算），则需区分设备原始数据与基准站数据；若仅存在设备位置数据且无法通过后处理技术校正（例如GPX格式文件），则无需区分基准与设备数据，文件直接置于GPS目录根目录下。 │ ├── BASE：来自RTK基站或其他静态定位仪器的文件。 │ └── DEVICE：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：用于存储当前任务数据处理结果的各类子目录。 │ ├── BATHY：从任务日志中提取的水深测量原始数据的输出目录。 │ ├── FRAMES：从DCIM目录下的视频中提取的带地理参考帧的输出目录。 │ ├── IA：图像识别预测结果的存储目录。 │ └── PHOTOGRAMMETRY：摄影测量重建模型的存储目录。 └── SENSORS：存储其他来源文件的目录（例如回声测深仪获取的水深数据、自动驾驶仪日志文件、任务规划文件等）。 ### 软件说明 本数据集所有原始数据均通过我们的工作流完成处理，所有预测结果均由我们的推理管线生成。你可在本仓库中获取下载该数据集所需的全部脚本。愿你借助SeatizenDOI畅享这份数据集！