Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-12-07

This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-12-07. 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 26.95 GB of MP4 files, which were trimmed into 9128 frames (at 2997/1000 fps). The frames are georeferenced. 99.89% of these extracted images are useful and 0.11% 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: 96.84 %, Q2: 2.96 %, Q5: 0.2 % 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 0.142 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2023年12月7日在留尼旺岛赫尔米蒂奇海域采集。 科学家与公民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（Artificial Intelligence）模型，以实现影像内目标的自动识别预测。本项目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/栖息地识别预测及地图生成。 本数据集是包含海量水下与航空影像的大型数据集集合Seatizen Altas的一部分，相关方法、工具及科学目标已在专属数据论文中详述。 图像采集 本次采集任务的原始数据为26.95GB的MP4视频文件，经剪辑后提取出9128帧图像，帧率为2997/1000 fps（约2.997帧/秒）。所有提取帧均已完成地理配准。经Jacques模型（Jacques model）预测，其中99.89%的提取图像为有效样本，剩余0.11%为无效样本。针对有效样本帧，已通过DinoVd'eau模型（DinoVd'eau model）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）工作流进行处理，实现了厘米级的GPS定位精度。 基准站数据：来自RTK基站或可提供校正帧的静态定位设备的文件。 设备GPS终端：Emlid Reach M2 数据质量分级：Q1占比96.84%，Q2占比2.96%，Q5占比0.2% 测深数据 本数据集通过单波束测深仪ETC 400采集得到。仅保留带有Q1级GPS校正信息的测深数据，同时保留所有航点对应的测深点数据，并筛选水深估算值介于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对齐，若有可用的局部大地水准面数据，则进一步应用其进行高程转换。处理完成后，将测深数据投影至均匀网格中，生成栅格文件（raster）与矢量形状文件（shapefiles）。网格单元尺寸为0.142米。栅格与形状文件通过线性插值法生成，三维重建算法采用球支点算法（ballpivot）。 通用文件夹结构 YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number ├── DCIM：用于存储采集所得视频与照片文件的目录。 ├── 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；若仅存在设备定位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，文件直接放置于GPS目录根目录下。 │ ├── 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：存储本次采集任务通用信息文件的目录。 ├── PROCESSED_DATA：用于存储本次采集任务所有数据处理结果的目录组。 │ ├── BATHY：存储从任务日志中提取的原始测深数据的输出目录。 │ ├── FRAMES：存储从DCIM目录视频中提取的地理配准帧的输出目录。 │ ├── IA：存储图像识别预测结果的目标目录。 │ └── PHOTOGRAMMETRY：存储摄影测量重建模型的目标目录。 └── SENSORS：用于存储其他来源文件的目录（例如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我们自研的工作流进行处理，所有预测结果均由我们的推理管线生成。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享受本数据集的使用体验！