Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-09-26

This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-09-26. 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 82.56 GB of MP4 files, which were trimmed into 19507 frames (at 2997/1000 fps). The frames are georeferenced. 99.63% of these extracted images are useful and 0.37% 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: 9.94 %, Q2: 6.65 %, Q5: 83.41 % 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 30.0 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 0.273 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!本数据集于2023年9月26日由无人水面艇（Autonomous Surface Vehicle）在留尼旺岛赫尔米特奇海域采集。 科研人员与民众采集的水下或航拍影像，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（AI）模型，进而实现影像内目标的识别预测。本项目提供一套软硬件工具集，可用于海洋数据采集、物种/生境预测及地图生成。 本数据集隶属于涵盖海量水下与航拍影像的大型数据集Seatizen Altas。相关研究方法、工具集及科学目标已在专属数据论文中详述。 图像采集 本次采集任务共产生82.56 GB的MP4视频文件，经剪辑抽取出19507帧图像，帧率为2997/1000 fps（约2.997帧/秒）。所有抽取出的帧均已完成地理配准。经雅克（Jacques）模型预测，其中99.63%的提取图像为有效帧，剩余0.37%为无效帧。针对有效帧，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）工作流进行处理，可实现厘米级的GPS定位精度。 基准站文件：来自RTK固定GPS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模块。 数据质量分级：Q1占比9.94%，Q2占比6.65%，Q5占比83.41% 水深测量 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校正信息的测深数值，仅保留航点处的测深点数据，且仅保留水深估算值介于0.2米至30.0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转换，随后根据实际情况加入当地大地水准面校正。处理完成后，将数据投影至统一格网，生成栅格文件与Shapefile矢量文件。格网单元尺寸为0.273米。栅格与矢量文件通过线性插值生成，三维重建算法采用ballpivot算法。 标准文件夹结构 命名规则：YYYYMMDD_COUNTRYCODE-可选地点_设备_会话-编号 ├── DCIM：用于存储采集到的视频与照片文件的目录。 ├── GPS：用于存储所有定位相关文件的目录。若文件可通过后处理动态差分（PPK，如基于RINEX数据）进行校正，则需区分基准站文件与设备端文件；若仅存在设备端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端文件，直接将文件置于GPS目录根目录下。 │ ├── BASE：存储来自RTK基准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：存储本次采集任务通用信息文件的目录。 ├── PROCESSED_DATA：存储本次采集任务数据处理结果的目录集合。 │ ├── BATHY：存储从任务日志中提取的原始水深数据的输出目录。 │ ├── FRAMES：存储从DCIM目录视频中提取的地理配准帧图像的输出目录。 │ ├── IA：存储图像识别预测结果的目录。 │ └── PHOTOGRAMMETRY：存储摄影测量重建模型的目录。 └── SENSORS：存储其他来源文件的目录，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项目自研工作流完成处理，所有预测结果均由本项目的推理管线生成。你可在本仓库中获取下载该数据集所需的全部脚本文件。欢迎使用SeatizenDOI获取更多相关数据！