Underwater images collected by an Autonomous Surface Vehicle in St-Leu, Réunion - 2023-10-24

This dataset was collected by an Autonomous Surface Vehicle in St-Leu, Réunion - 2023-10-24. 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 44.02 GB of MP4 files, which were trimmed into 9207 frames (at 2997/1000 fps). The frames are georeferenced. 99.9% of these extracted images are useful and 0.1% 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: 92.79 %, Q2: 6.23 %, Q5: 0.98 % 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 0.213 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!本数据集由无人水面艇（Autonomous Surface Vehicle）于2023年10月24日在留尼汪岛圣勒（St-Leu）采集。 科研人员或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领域。此类影像经标注与共享后，可用于训练人工智能模型，以实现影像内目标的自动识别。本套件包含硬件与软件工具，可用于海洋数据采集、物种/生境预测以及地图制作。 本数据集隶属于包含海量水下与航空影像的Seatizen图集（Seatizen Atlas）完整数据集。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。 ### 影像采集 本次采集任务包含44.02 GB的MP4视频文件，经剪辑后提取出9207帧图像（帧率为2997/1000 fps）。所有提取帧均已完成地理配准。根据雅克模型（Jacques model）的预测结果，本次提取的影像中99.9%为有效帧，仅0.1%为无效帧。针对有效帧，已通过DinoVd'eau模型完成多标签预测。 ### GPS信息 本次数据采用后处理动态（PPK）工作流进行处理，以实现厘米级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。 数据质量分级：Q1占比92.79%，Q2占比6.23%，Q5占比0.98%。 ### 水深测量 本次数据采用单波束测深仪S500进行采集。仅保留带有Q1级GPS校正的测点数据，同时保留航点测点。仅保留水深估算值介于0.2米至50.0米之间的原始数据。数据首先以WGS84椭球面为基准进行参考校准，若存在本地大地水准面则进一步应用其进行修正。处理完成后，数据被投影至均匀网格以生成栅格文件与shapefiles，网格单元尺寸为0.213米。栅格与shapefiles通过线性插值生成，三维重建算法采用ballpivot算法。 ### 通用文件夹结构 文件夹命名格式为：YYYYMMDD_COUNTRYCODE-[可选地点]_device_session-number ├── DCIM：用于存储采集所得视频与照片的目录，根据采集介质类型存放对应文件。 ├── GPS：存储所有与定位相关的文件目录。若文件可通过后处理技术进行校正（例如基于RINEX数据的后处理动态差分），则需区分设备数据与基准站数据；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，直接将文件置于GPS目录根目录下。 │ ├── BASE：存储来自RTK基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：存储本次任务数据处理结果的目录，包含以下子文件夹： │ ├── BATHY：用于存放从任务日志中提取的水深测量原始数据的输出目录。 │ ├── FRAMES：用于存放从DCIM目录视频中提取的地理配准帧图像的输出目录。 │ ├── IA：用于存放图像识别预测结果的目录。 │ └── PHOTOGRAMMETRY：用于存放摄影测量重建模型的目录。 └── SENSORS：存储其他来源文件的目录，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流水线生成。您可在本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获取的数据集顺利！