Underwater images collected by an Autonomous Underwater Vehicle in Hermitage, Réunion - 2021-11-26

This dataset was collected by an Autonomous Underwater Vehicle in Hermitage, Réunion - 2021-11-26. 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. This dataset was collected by an Autonomous Underwater Vehicle, Réunion - 2021-12 (project RECIF 3D) This dataset was processed with tools developped by different subsequent projects - 2025-12 (projects PLANCHA, ...) 3D reconstruction and mapping of Reunion coral ecosystems from underwater images. Survey information Camera: Prosilica Number of images: 842 Total size: 11.47 Gb Flight start: 2021:11:26 04:25:04 Flight end: 2021:11:26 04:39:06 Flight duration: 0h 14min 2sec Max depth: 13.86 m 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. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!本数据集由自主水下航行器（Autonomous Underwater Vehicle）于留尼旺岛赫尔米塔奇地区采集，采集日期为2021年11月26日。 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。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目标识别。本项目提供一套软硬件工具集，可用于海洋数据采集、物种/生境预测及地图绘制。 本数据集隶属于涵盖大量水下与航空影像的大型数据集集合Seatizen Atlas。相关研究方法、工具及科学目标已在专属数据论文中详细阐述。 本数据集由自主水下航行器于留尼旺岛2021年12月采集（项目代号RECIF 3D）。 本数据集经后续多个项目开发的工具进行处理，处理时间为2025年12月（涉及项目包括PLANCHA等）。 本数据集基于水下影像完成留尼旺岛珊瑚生态系统的三维重建与制图。 ## 调查信息 - 相机型号：Prosilica - 影像总数：842张 - 总大小：11.47 GB - 作业开始时间：2021-11-26 04:25:04 - 作业结束时间：2021-11-26 04:39:06 - 作业时长：0小时14分2秒 - 最大作业深度：13.86米 ## 通用文件夹结构 命名格式：YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number ├── DCIM：用于存储采集到的视频与照片等媒体文件的目录。 ├── 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数据的后处理运动学校正），则需区分基准站数据与设备端数据；若仅存在设备端定位数据且无法通过后处理技术校正（例如GPX文件），则无需区分基准站与设备端数据，直接将文件放置于GPS目录根目录下。 │ ├── BASE：存储来自RTK基站或其他静态定位仪器的数据文件。 │ └── DEVICE：存储来自采集设备的定位数据文件。 ├── METADATA：存储本次作业通用信息文件的目录。 ├── PROCESSED_DATA：用于存储当前作业数据处理结果的各类子目录。 │ ├── 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目录中的视频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开发的工作流进行处理。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进行数据研究顺利！