Underwater images collected by an Autonomous Surface Vehicle in Tessier, Réunion - 2024-04-05

This dataset was collected by an Autonomous Surface Vehicle in Tessier, Réunion - 2024-04-05. 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 30.54 GB of MP4 files, which were trimmed into 11362 frames (at 2997/1000 fps). The frames are georeferenced. 99.65% of these extracted images are useful and 0.35% 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: 93.52 %, Q2: 5.31 %, Q5: 1.17 % 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 2.0 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 0.137 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载具（Autonomous Surface Vehicle）于2024年4月5日在留尼旺岛泰西耶（Tessier, Réunion）采集。 科学家与民众采集的水下及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。这些图像可经标注后共享，用于训练人工智能模型以实现图像内目标的识别预测。本团队提供一套软硬件工具集，可用于海洋数据采集、物种/栖息地识别预测及地图生成。 本数据集隶属于包含大量水下及航拍图像的大型数据集集合Seatizen Altas。相关研究方法、工具及科学目标已在专用数据集论文中详述。 图像采集 本次采集任务包含30.54 GB的MP4视频文件，经剪辑提取出11362帧图像（帧率为2997/1000 fps）。所有提取帧均已完成地理配准。经Jacques模型预测，其中99.65%的提取图像为有效样本，剩余0.35%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信息 本数据集采用后处理运动学（PPK, Post-Processed Kinematic）处理流程，以实现厘米级GPS定位精度。 基准站（Base）：来自RTK（实时动态差分）GPS固定站或可提供校正帧的静态定位设备的文件。 设备GPS：Emlid Reach M2 数据质量分级：Q1占比93.52%，Q2占比5.31%，Q5占比1.17%。 水深测量 测深数据通过单波束回声测深仪ETC 400采集。仅保留带有Q1级GPS校正的测深数值，同时保留所有航点数据点，并保留水深估算值介于0.2米至2.0米之间的原始数据。数据首先以WGS84椭球面为基准进行配准，随后根据需要使用局部大地水准面进行校正。数据处理完成后，将其投影至统一网格以生成栅格文件与形状文件（Shapefiles）。网格单元尺寸为0.137米。栅格与形状文件通过线性插值生成，三维重建采用球枢（ballpivot）算法。 通用文件夹结构 YYYYMMDD_国家代码-[可选地点]_设备_任务-编号 ├── DCIM：用于存储采集到的视频与照片的文件夹。 ├── 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数据的后处理运动学校正）进行定位校正，则需区分设备数据与基准站数据；若仅存在设备位置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：存储本次采集任务通用信息文件的文件夹。 ├── PROCESSED_DATA：存储本次采集任务数据处理结果的所有子文件夹。 │ ├── BATHY：从任务日志中提取的测深原始数据输出文件夹。 │ ├── FRAMES：从DCIM文件夹视频中提取的地理配准帧输出文件夹。 │ ├── IA：图像识别预测结果的存储文件夹。 │ └── PHOTOGRAMMETRY：摄影测量重建模型的存储文件夹。 └── SENSORS：存储其他来源文件的文件夹（如回声测深仪采集的测深数据、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本团队开发的处理流程完成校正，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。祝您使用SeatizenDOI开展研究顺利！