Underwater images collected by an Autonomous Underwater Vehicle in Boucan, Réunion - 2021-11-30

This dataset was collected by an Autonomous Underwater Vehicle in Boucan, Réunion - 2021-11-30. 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: 1860 Total size: 22.37 Gb Flight start: 2021:11:30 12:09:23 Flight end: 2021:11:30 12:58:11 Flight duration: 0h 48min 48sec Max depth: 18.12 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', '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）于留尼旺岛布坎海域采集，采集时间为2021年11月30日。 科学家或公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、物种或生境识别预测，以及专题地图制作。 本数据集是收录大量水下与航空影像的大型集合Seatizen Altas的一部分，相关方法、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采集，采集时段为2021年12月（项目代号：RECIF 3D）；本数据集经多个后续项目开发的工具处理，处理时段为2025年12月（涉及项目包括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制图。 ## 调查信息 - 相机型号：Prosilica - 影像总数：1860张 - 总数据量：22.37 GB - 航行开始时间：2021:11:30 12:09:23 - 航行结束时间：2021:11:30 12:58:11 - 航行时长：0小时48分48秒 - 最大作业深度：18.12米 ## 摄影测量 本研究采用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存在差异的配置项。如需了解更多细节，可查阅log.json日志文件或000_photogrammatry_report.pdf报告。 配置参数如下： {'auto_boundary': True, 'cog': True, 'fast_orthophoto': True, 'feature_quality': 'ultra', '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-optionalplace_device_session-number ├── DCIM：用于存储采集到的视频与照片的文件夹，具体存储内容取决于采集的媒体类型。 ├── 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RINEX数据的后处理运动学校正），则需区分基准站数据与设备数据；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文件），则无需区分基准站与设备数据，文件直接放置于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文件夹。 ├── PROCESSED_DATA：用于存储当前会话数据处理结果的所有子文件夹。 │ ├── BATHY：从任务日志中提取的水深原始数据的输出文件夹。 │ ├── FRAMES：从DCIM文件夹的视频中提取的地理参考帧的输出文件夹。 │ ├── IA：图像识别预测结果的目标存储文件夹。 │ └── PHOTOGRAMMETRY：摄影测量重建模型的目标存储文件夹。 └── 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水深数据、自动驾驶仪的日志文件、任务规划文件等）。 ## 软件工具 所有原始数据均通过我们的工作流进行处理。您可在本代码仓库中找到下载该数据集所需的全部脚本。祝您使用SeatizenDOI开展研究顺利！