Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-11-24

This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-11-24. 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 34.0 GB of MP4 files, which were trimmed into 12719 frames (at 2997/1000 fps). The frames are georeferenced. 35.77% of these extracted images are useful and 64.23% 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: 96.86 %, Q2: 2.85 %, Q5: 0.29 % Bathymetry The data are collected using a single-beam echosounder S500. 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 0.786 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11月24日在留尼旺岛（Réunion）的赫尔米特（Hermitage）由自主水面航行器（Autonomous Surface Vehicle）采集。 科学家或民众采集的水下、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。对这些影像进行标注并共享后，可用于训练人工智能模型，进而实现影像内目标的自动识别预测。本项目提供一套软硬件工具集，可用于海洋数据采集、物种/栖息地识别预测以及地图生成。 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 Atlas的一部分。相关研究方法、工具集与科学目标已在专门的数据论文中进行了详细阐述。 图像采集 本次采集任务包含34.0 GB的MP4视频文件，经剪辑后提取得到12719帧图像（帧率为2997/1000 fps）。所有提取帧均已完成地理配准。经雅克模型（Jacques model）预测，其中35.77%的提取图像为有效帧，64.23%为无效帧。针对有效帧，已使用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）工作流进行处理，以实现厘米级的GPS定位精度。 基准站文件：来自RTK（实时动态差分，Real-Time Kinematic）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 数据质量分布：Q1占比96.86%，Q2占比2.85%，Q5占比0.29% 水深测量 本数据集通过单波束测深仪S500采集水深数据。仅保留带有Q1级GPS校正信息的数据值，同时保留所有航点数据点，并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。处理完成后，数据被投影至统一格网中，生成栅格数据与Shapefile文件，格网单元尺寸为0.786米。栅格与Shapefile文件通过线性插值生成，三维重建算法采用球枢（ballpivot）算法。 通用文件夹结构 通用命名格式：YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number ├── DCIM：用于存储采集所得视频与照片的文件夹。 ├── GPS：用于存储所有与定位相关的文件。若文件可通过各类方式进行校正（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，并分别存储；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，文件直接存放于GPS文件夹根目录下。 │ ├── BASE：存放来自RTK基准站或静态定位设备的文件。 │ └── DEVICE：存放来自采集设备的定位文件。 ├── METADATA：用于存储本次采集任务通用信息文件的文件夹。 ├── PROCESSED_DATA：用于存储本次任务数据处理所得全部结果的文件夹。 │ ├── BATHY：存储从任务日志中提取的水深原始数据的输出文件夹。 │ ├── FRAMES：存储从DCIM视频中提取的地理配准帧的输出文件夹。 │ ├── 人工智能（IA）：存储图像识别预测结果的目标文件夹。 │ └── PHOTOGRAMMETRY：存储摄影测量重建模型的目标文件夹。 └── SENSORS：用于存储其他来源文件的文件夹，例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。 软件工具 所有原始数据均通过本团队开发的工作流进行处理，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顺利！