Underwater images collected by an Autonomous Surface Vehicle in St-Leu, Réunion - 2025-11-04

This dataset was collected by an Autonomous Surface Vehicle in St-Leu, Réunion - 2025-11-04. 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 16.35 GB of MP4 files, which were trimmed into 5289 frames (at 2997/1000 fps). The frames are georeferenced. 100.0% of these extracted images are useful and 0.0% 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: 84.86 %, Q2: 15.01 %, Q5: 0.12 % Bathymetry The data are collected using a single-beam echosounder S500. We only keep the values which have a GPS correction in Q1. We keep the points that are the waypoints. We keep the raw data where depth was estimated between 0.2 m and 50.0 m deep. The data are first referenced against the WGS84 ellipsoid. Then we apply the local geoid if available. At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles. The size of the grid cells is 0.359 m. The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot. 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航行器（Autonomous Surface Vehicle）于留尼旺圣勒采集，采集日期为2025年11月4日。 科学家或民众采集的水下与航拍图像可广泛应用于科学研究、资源管理与生态保护领域。此类图像可经标注后共享，用于训练人工智能（AI）模型，进而实现图像内目标的自动识别。我们提供一套包含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生成。 本数据集隶属于包含大量水下与航拍图像的大型集合Seatizen Atlas。相关方法、工具及科学目标已在专门的数据论文中详述。 ## 图像采集 本次采集任务生成了总容量16.35GB的MP4视频文件，经裁切后得到5289帧图像，帧率为2997/1000 fps（约2.997帧每秒）。所有帧均已完成地理配准。 经雅克（Jacques）模型预测，提取出的图像中100.0%为有效样本，0.0%为无效样本。针对有效帧，已通过DinoVd'eau模型完成多标签分类预测。 ## GPS信息 本数据集采用后处理运动学（Post-Processed Kinematic，PPK）工作流进行处理，以实现厘米级的GPS定位精度。 - 基准站数据：来自RTK固定GPS站或可提供校正帧的静态定位设备。 - 设备GPS接收机：采用Emlid Reach M2型号。 - 数据质量分布：Q1级占比84.86%，Q2级占比15.01%，Q5级占比0.12% ## 水深测量 本数据集通过单波束测深仪S500采集水深数据。 我们仅保留带有Q1级GPS校正的测深测点数据，同时保留航迹点数据。 仅保留水深估算值介于0.2米至50.0米之间的原始测深数据。 数据首先以WGS84椭球体（WGS84 ellipsoid）为基准进行参考定位，随后根据可用情况应用局部大地水准面（geoid）校正。 处理完成后，数据被投影至统一网格以生成栅格数据（raster）与形状文件（shapefiles），网格单元尺寸为0.359米。栅格数据与形状文件通过线性插值生成，三维重建算法采用球枢算法（ballpivot）。 ## 通用文件夹命名与结构 文件夹命名格式为：YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number ├── DCIM：用于存储采集到的视频与照片文件，依媒体类型分类。 ├── 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，直接将文件置于GPS文件夹根目录下。 │ ├── BASE：来自RTK基准站或静态定位设备的数据文件。 │ └── DEVICE：来自采集设备的数据文件。 ├── METADATA：存储本次采集任务通用信息文件的文件夹。 ├── PROCESSED_DATA：用于存储本次采集任务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原始测深数据输出目录。 │ ├── FRAMES：从DCIM视频中提取的地理配准帧图像输出目录。 │ ├── IA：图像识别预测结果的存储目录。 │ └── PHOTOGRAMMETRY：摄影测量重建模型的存储目录。 └── SENSORS：用于存储其他来源文件的文件夹，例如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。 ## 软件说明 所有原始数据均通过我们的工作流进行处理，所有预测结果均由我们的推理管线生成。你可在本代码仓库中获取下载该数据集所需的全部脚本。使用本数据集时请参考SeatizenDOI！