Underwater images collected by Scuba diving in Hermitage, Réunion - 2021-03-17

This dataset was collected by Scuba diving in Hermitage, Réunion - 2021-03-17. 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. 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!本数据集采集于2021年3月17日留尼旺岛（Réunion）隐士礁（Hermitage）区域的水肺潜水作业。 科研人员与公众采集的水下或航空影像，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（AI）模型，进而实现影像内目标的自动识别与预测。 我们提供一套涵盖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，以及地图制作。 通用文件夹结构 YYYYMMDD_COUNTRYCODE-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文件的文件夹。 ├── GPS：用于存储所有与定位相关文件的文件夹。若可对文件进行位置校正（例如基于RINEX数据进行后处理运动学校正），则需区分基站数据与设备端数据；若仅存在设备端位置数据且无法通过后处理技术校正（例如GPX格式文件），则无需区分基站与设备端数据，直接将文件置于GPS文件夹根目录下。 │ ├── BASE：存储来自RTK基站或其他静态定位仪器的文件。 │ └── DEVICE：存储来自采集设备的文件。 ├── METADATA：存储本次作业会话通用信息文件的文件夹。 ├── PROCESSED_DATA：用于存储当前会话数据处理结果的文件夹集合。 │ ├── BATHY：存储从作业日志中提取的水深测量原始数据的输出文件夹。 │ ├── FRAMES：存储从DCIM文件夹内视频中提取的地理参考帧的输出文件夹。 │ ├── IA：存储图像识别预测结果的目标文件夹。 │ └── PHOTOGRAMMETRY：存储摄影测量重建模型的目标文件夹。 └── SENSORS：存储其他来源文件的文件夹，例如回声测深仪获取的水深数据、自动驾驶仪日志文件、作业规划文件等。 软件说明 所有原始数据均通过我们自研的工作流完成处理，所有预测结果均由我们的推理管线生成。用户可在本仓库中获取下载该数据集所需的全部脚本。请通过SeatizenDOI进行数据使用授权，祝您使用顺利！