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 12.06 GB of MP4 files, which were trimmed into 1276 frames (at 2997/1000 fps). The frames are georeferenced. 99.22% of these extracted images are useful and 0.78% 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: 53.75 %, Q2: 7.14 %, Q5: 39.11 % Bathymetry The data are collected using a single-beam echosounder ETC 400. 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 0.683 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!本数据集由无人水面航行器（Autonomous Surface Vehicle）于留尼旺岛拉萨利讷采集，采集日期为2025年5月15日。 科研人员与民众采集的水下及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。这些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预测。本团队提供一套软硬件工具集，可用于海洋数据采集、物种/栖息地识别预测以及地图生成。 本数据集是包含海量水下及航空影像的大型数据集集合Seatizen Altas的组成部分。相关研究方法、工具集与科学目标已在专属数据论文中详细阐述。 图像采集 本次采集任务共产生12.06GB的MP4视频文件，经剪辑提取得到1276帧影像（帧率为2997/1000 fps）。所有提取帧均已完成地理配准。根据雅克模型（Jacques model）的预测结果，其中99.22%的提取影像为有效样本，剩余0.78%为无效样本。针对有效样本帧，已通过DinoVd'eau模型完成多标签预测。 GPS定位信息 本数据集采用后处理动态差分（PPK, Post-Processed Kinematic）流程处理，以实现厘米级的GPS定位精度。 基准站数据：指来源于固定GPS基站或可提供校正帧的静态定位设备的文件。 设备GPS：采用Emlid Reach M2设备。 数据质量分级：Q1占比53.75%，Q2占比7.14%，Q5占比39.11%。 水深测量 本次水深数据通过单波束测深仪ETC 400采集得到。仅保留带有Q1级GPS校正的测深数据，且仅保留航迹点对应的测深数据，同时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椭球面为基准进行坐标系转换，若可用则进一步应用局部大地水准面模型校正。处理完成后，数据将被投影至统一格网，生成栅格文件与矢量形状文件（shapefiles），格网单元尺寸为0.683米。栅格与形状文件通过线性插值算法生成，三维重建算法采用球枢（ballpivot）算法。 通用文件夹结构 命名规则：YYYYMMDD_COUNTRYCODE-可选地点_设备_会话-编号 ├── DCIM：用于存储采集得到的视频与照片文件，依采集介质类型存放。 ├── GPS：用于存储所有与定位相关的文件。若文件可通过后处理技术（如基于RINEX数据的后处理动态差分）进行校正，则需区分基准站数据与设备数据；若仅存在设备定位数据且无法通过后处理技术校正（如gpx文件），则无需区分基准站与设备数据，文件直接存放于GPS目录根目录下。 │ ├── BASE：存储来自RTK（实时动态差分，Real-Time Kinematic）基站或静态定位设备的文件。 │ └── DEVICE：存储来自采集设备的定位文件。 ├── METADATA：存储本次采集任务的通用信息文件。 ├── PROCESSED_DATA：用于存储本次采集任务的所有数据处理结果。 │ ├── BATHY：存储从任务日志中提取的原始水深数据。 │ ├── FRAMES：存储从DCIM目录视频中提取的已地理配准的影像帧。 │ ├── IA：存储图像识别预测结果文件。 │ └── PHOTOGRAMMETRY：存储摄影测量重建得到的三维模型文件。 └── SENSORS：存储其他来源的文件（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本团队自研工作流完成处理，所有预测结果均由本团队的推理管线生成。您可在本代码仓库中获取下载该数据集所需的全部脚本文件。祝您使用SeatizenDOI数据集愉快！