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: 603 Total size: 7.6 Gb Flight start: 2021:11:26 04:55:07 Flight end: 2021:11:26 05:05:09 Flight duration: 0h 10min 2sec Max depth: 10.92 m Photogrammetry OpenDroneMap software was used to create an orthophoto from the raw images. Here is the list of parameters different from the default values for the orthophoto generation. For more details, you can read the log.json file or the 000_photogrammatry_report.pdf report. {'auto_boundary': True, 'cog': True, 'fast_orthophoto': True, 'feature_quality': 'ultra', 'gps_accuracy': 0.1, 'max_concurrency': 50, 'optimize_disk_space': True, 'orthophoto_resolution': 0.1, 'skip_3dmodel': True} 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2021年11月26日在留尼旺岛赫尔米特奇海域采集。 科学家或公民科学家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。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、物种或生境识别以及地图绘制。 本数据集是包含大量水下与航空影像的大型数据集集合Seatizen Altas的一部分。相关方法、工具及科学目标已在专门的数据论文中进行阐述。 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），并于2025年12月由后续不同项目开发的工具完成处理（涉及项目PLANCHA等）。本数据集旨在通过水下影像实现留尼旺岛珊瑚生态系统的三维重建与地图绘制。 ### 调查信息 - 相机型号：Prosilica - 影像总数：603张 - 总数据量：7.6 GB - 作业开始时间：2021年11月26日 04:55:07 - 作业结束时间：2021年11月26日 05:05:09 - 作业时长：0小时10分钟2秒 - 最大作业深度：10.92米 ### 摄影测量 本研究使用OpenDroneMap软件从原始影像中生成正射影像。以下为正射影像生成过程中与默认参数不同的配置项。如需了解更多细节，可查阅log.json文件或000_photogrammatry_report.pdf报告。 {"auto_boundary": true, "cog": true, "fast_orthophoto": true, "feature_quality": "ultra", "gps_accuracy": 0.1, "max_concurrency": 50, "optimize_disk_space": true, "orthophoto_resolution": 0.1, "skip_3dmodel": true} ### 通用文件夹结构 文件夹命名格式：YYYYMMDD_COUNTRYCODE-可选地点_设备_会话_编号 ├── DCIM：用于存储采集到的视频与照片的文件夹，具体内容取决于采集的介质类型。 ├── 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（例如基于星历数据的后处理运动学校正），则需区分基准站数据与设备采集数据；若仅存在设备位置数据且无法通过后处理技术进行校正（例如gpx文件），则无需区分基准站与设备数据，直接将文件放置于GPS文件夹根目录。 │ ├── BASE：来自RTK基站或其他静态定位仪器的数据文件。 │ └── DEVICE：来自采集设备的数据文件。 ├── METADATA：存储本次采集会话通用信息文件的元数据文件夹。 ├── PROCESSED_DATA：用于存储当前会话数据处理结果的文件夹集合，包含以下子目录： │ ├── BATHY：从任务日志中提取的水深原始数据输出目录。 │ ├── FRAMES：从DCIM文件夹的视频中提取的带地理坐标帧的输出目录。 │ ├── 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，例如回声测深仪采集的水深数据、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完成处理。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开展研究顺利！