Underwater images collected by an Autonomous Surface Vehicle in Grandfond, Réunion - 2023-02-03

This dataset was collected by an Autonomous Surface Vehicle in Grandfond, Réunion - 2023-02-03. 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 16.77 GB of MP4 files, which were trimmed into 7782 frames (at 2997/1000 fps). The frames are georeferenced. 99.36% of these extracted images are useful and 0.64% 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: 1.19 %, Q2: 27.23 %, Q5: 71.58 % 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 autofilled 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2月3日在留尼旺岛的Grandfond采集。 科研人员或普通民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。此类影像可经标注后共享，用于训练人工智能（AI）模型以实现图像内目标的识别预测。我们提供一套涵盖硬件与软件的工具集，用于海洋数据采集、物种或生境预测以及地图生成。 本数据集是涵盖大量水下与航空影像的Seatizen Altas大型数据集的组成部分。相关研究方法、工具集与科学目标已在专属数据论文中详细阐述。 ### 图像采集 本次采集任务共生成16.77 GB的MP4视频文件，经剪辑后提取出7782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99.36%的提取图像为有效样本，剩余0.64%为无效样本。针对有效帧，我们使用DinoVd'eau模型完成了多标签预测。 ### GPS信息 本数据采用后处理动态定位（PPK, Post-Processed Kinematic）工作流进行处理，以实现厘米级的GPS定位精度。 - 基准站数据：来自RTK固定GPS基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。 本数据的质量分级如下：Q1占比1.19%，Q2占比27.23%，Q5占比71.58%。 ### 水深测量 本数据通过单波束测深仪ETC 400采集。我们仅保留那些带有Q1级GPS校正的数值，同时保留航点数据。我们还保留了水深估算值介于0.2米至50.0米之间的原始数据。数据首先以WGS84椭球面（WGS84 ellipsoid）为基准进行参考对齐，随后在可用时使用当地大地水准面（geoid）进行校正。处理完成后，数据将被投影至统一网格以生成栅格文件（raster）与形状文件（shapefiles）。网格单元大小将自动填充，单位为米。栅格与形状文件通过线性插值生成，三维重建算法采用ballpivot。 ### 通用文件夹结构 文件夹命名格式为：YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number ├── 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文件的文件夹。若可对文件进行校正（例如基于RINEX数据的后处理动态定位），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进行校正（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文件夹根目录。 │ ├── BASE：来自RTK基站或静态定位设备的文件。 │ └── DEVICE：来自采集设备的文件。 ├── METADATA：存储本次采集任务通用信息文件的文件夹。 ├── PROCESSED_DATA：包含存储当前采集任务数据处理结果所需的所有子文件夹。 │ ├── BATHY：用于存储从任务日志中提取的水深原始数据的输出文件夹。 │ ├── FRAMES：用于存储从DCIM视频中提取的地理配准帧的输出文件夹。 │ ├── IA：用于存储图像识别预测结果的目标文件夹。 │ └── PHOTOGRAMMETRY：用于存储摄影测量（photogrammetry）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我们的工作流完成处理，所有预测结果均由我们的推理流水线（inference pipeline）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进行数据研究顺利！