Underwater images collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-07

This dataset was collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-07. 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 78.22 GB of MP4 files, which were trimmed into 17724 frames (at 2997/1000 fps). The frames are georeferenced. 29.87% of these extracted images are useful and 70.13% 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: 45.47 %, Q2: 8.05 %, Q5: 46.48 % 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 0.68 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艇（Autonomous Surface Vehicle）于2023年9月7日在留尼旺岛（Réunion）的布坎（Boucan）海域采集。 由科研人员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。这类影像可经标注后共享，用于训练人工智能（Artificial Intelligence）模型，以实现影像内目标的自动识别与预测。本项目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/栖息地识别预测以及地图生成。 本数据集是收录大量水下与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述。 ### 影像采集 本次采集任务包含78.22GB的MP4视频文件，经裁切后得到17724帧图像（帧率为29.97 fps，即2997/1000 fps）。所有帧数据均已完成地理配准。根据Jacques模型的预测结果，本次提取的影像中29.87%为有效影像，70.13%为无效影像。针对有效帧，已通过DinoVd'eau模型完成多标签预测。 ### GPS信息 本数据集采用后处理运动学（Post-Processing 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：来自实时动态差分（Real-Time Kinematic, RTK）GPS固定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。 本数据集的质量分布为：Q1占比45.47%，Q2占比8.05%，Q5占比46.48%。 ### 水深测量 本次水深数据采用单波束测深仪ETC 400采集。仅保留Q1等级且带有GPS校正信息的数据点，以及航迹点数据。同时筛选出水深估算值介于0.2米至50.0米之间的原始数据。 数据处理流程为先以WGS84椭球面为基准进行参考对齐，若存在局部大地水准面则应用其进行校正。处理完成后，将数据投影至统一格网以生成栅格文件与形状文件（Shapefile），格网单元尺寸为0.68米。栅格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（Ball Pivot）。 ### 标准文件夹结构 命名格式：`YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number` ├── DCIM/ # 用于存储采集到的视频与照片文件 ├── GPS/ # 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数据的后处理运动学校正）获得校正信息，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文件夹根目录 │ ├── BASE/ # 来自RTK基准站或静态定位设备的文件 │ └── DEVICE/ # 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/# 存储本次任务所有数据处理结果的文件夹 │ ├── BATHY/ # 从任务日志中提取的水深原始数据输出目录 │ ├── FRAMES/ # 从DCIM视频中提取的已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/ # 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、任务规划文件等） ### 软件工具 所有原始数据均通过本项目的工作流进行处理，所有预测结果均由本项目的推理管线生成。您可在本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！