Underwater images collected by an Autonomous Surface Vehicle in La-Saline, Réunion - 2025-05-15

This dataset was collected by an Autonomous Surface Vehicle in La-Saline, Réunion - 2025-05-15. 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. Image acquisition This session has 31.86 GB of MP4 files, which were trimmed into 12123 frames (at 2997/1000 fps). The frames are georeferenced. 99.7% of these extracted images are useful and 0.3% are useless, according to predictions made by Jacques model. Multilabel predictions have been made on useful frames using DinoVd'eau model. GPS information: The data was processed with a PPK workflow to achieve centimeter-level GPS accuracy. Base : Files coming from rtk a GPS-fixed station or any static positioning instrument which can provide with correction frames. Device GPS : Emlid Reach M2 Quality of our data - Q1: 55.71 %, Q2: 8.06 %, Q5: 36.23 % 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）于留尼汪岛拉萨林采集，采集日期为2025年5月15日。 科研人员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。此类影像经标注后可共享，用于训练人工智能模型以自动识别影像中的目标对象。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，并辅助生成专题地图。 本数据集隶属于涵盖大量水下与航空影像的大型合集Seatizen Atlas（原文疑似存在拼写笔误，原文本为Seatizen Altas）。相关方法、工具与科学目标已在专属数据论文中详细阐述。 图像采集 本次采集会话共生成31.86 GB的MP4格式文件，经剪辑后得到12123帧影像帧，帧率为2997/1000 fps。所有影像帧均已完成地理参考。根据雅克模型（Jacques model）的预测结果显示，其中99.7%的提取影像为有效帧，剩余0.3%为无效帧。针对有效帧，我们已通过DinoVd'eau模型完成多标签识别任务。 GPS定位信息 本数据集采用PPK（后处理运动学，Post-Processed Kinematic）工作流，以实现厘米级的GPS定位精度。 基准站文件：来自RTK（实时动态差分定位，Real-Time Kinematic）固定GPS基站或可提供校正帧的静态定位设备。 设备GPS模块：Emlid Reach M2。 数据质量分布：Q1占比55.71%，Q2占比8.06%，Q5占比36.23%。 通用文件夹结构 统一命名格式为YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number ├── DCIM：用于存储采集所得的视频与照片，依媒体类型分类存放。 ├── GPS：用于存储所有与定位相关的文件。若可对文件进行校正（例如通过RINEX数据进行后处理运动学校正），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，直接将文件存放于GPS文件夹根目录。 │ ├── BASE：来自RTK基站或静态定位设备的定位文件。 │ └── DEVICE：来自采集设备的定位文件。 ├── METADATA：用于存储本次采集会话的通用信息文件。 ├── PROCESSED_DATA：用于存储本次会话数据处理结果的相关子文件夹。 │ ├── BATHY：用于存储从任务日志中提取的测深原始数据。 │ ├── FRAMES：用于存储从DCIM文件夹视频中提取的地理参考影像帧。 │ ├── IA：用于存储图像识别预测结果。 │ └── PHOTOGRAMMETRY：用于存储摄影测量重建的三维模型。 └── SENSORS：用于存储其他来源的文件（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）。 软件说明 所有原始数据均通过我们的工作流完成处理，所有预测结果均由我们的推理管线生成。您可在本代码仓库中获取下载该数据集所需的全部脚本。祝您使用本数据集愉快，欢迎引用SeatizenDOI！