Underwater images collected by an Autonomous Surface Vehicle in St-Leu, Réunion - 2023-10-21

This dataset was collected by an Autonomous Surface Vehicle in St-Leu, Réunion - 2023-10-21. 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载具（Autonomous Surface Vehicle）于2023年10月21日在留尼汪圣勒（St-Leu）采集。 科学家或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。对这些影像进行标注并共享后，可用于训练人工智能模型，进而实现影像内目标的识别预测。本团队提供一套包含硬件与软件的工具集，可用于海洋数据采集、物种或栖息地识别预测，以及地图生成。 本数据集是收录海量水下与航空影像的大型数据集集合Seatizen Altas的一部分。相关研究方法、工具集与科学目标已在专设的数据论文中详述。 通用文件夹结构 YYYYMMDD_国家代码-可选地点_设备_会话-编号 ├── DCIM：用于存储采集所得的各类视频与照片的文件夹。 ├── GPS：用于存储所有与定位相关的文件。若可对文件进行各类校正（例如基于RINEX格式数据进行后处理运动学校正），则需区分设备数据与基准站数据；若仅存在设备定位数据且无法通过后处理技术校正文件（例如GPX文件），则无需区分基准站与设备数据，直接将文件置于GPS文件夹根目录下。 │ ├── BASE：来自RTK基准站或其他静态定位仪器的文件。 │ └── DEVICE：来自采集设备的文件。 ├── METADATA：存储本次采集会话通用信息文件的文件夹。 ├── PROCESSED_DATA：用于存储本次会话数据处理结果的所有子文件夹。 │ ├── BATHY：用于存放从任务日志中提取的测深原始数据的输出文件夹。 │ ├── FRAMES：用于存放从DCIM文件夹内视频中提取的地理参考帧的输出文件夹。 │ ├── IA：用于存储图像识别预测结果的目标文件夹。 │ └── PHOTOGRAMMETRY：用于存储摄影测量重建模型的目标文件夹。 └── SENSORS：用于存储其他来源文件的文件夹（例如回声测深仪获取的测深数据、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本团队的工作流进行处理，所有预测结果均由本团队的推理流水线生成。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愉快！