Underwater images collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-11-20

This dataset was collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-11-20. 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 31.07 GB of MP4 files, which were trimmed into 10888 frames (at 2997/1000 fps). The frames are georeferenced. 87.95% of these extracted images are useful and 12.05% 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: 94.26 %, Q2: 4.82 %, Q5: 0.92 % 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 0.642 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航行器于2023年11月20日在留尼汪岛布坎海域采集。 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。此类影像经标注与共享后，可用于训练人工智能（Artificial Intelligence）模型，以实现影像内目标的自动识别与预测。我们提供一套包含硬件与软件的工具集，可用于海洋数据采集、物种或生境识别预测以及地图制作。 本数据集隶属于Seatizen Atlas这一涵盖大量水下与航空影像的大型数据集集合。相关研究方法、工具集及科学目标已在专门的数据论文中详细阐述。 ### 图像采集 本次采集任务生成总容量31.07GB的MP4视频文件，经剪辑分割后提取得到10888帧图像（帧率为2997/1000 fps，即约2.997帧/秒）。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，其中87.95%的提取图像为有效样本，12.05%为无效样本。针对有效帧，我们已使用DinoVd'eau模型完成多标签预测标注。 ### GPS定位信息 本数据集采用后处理运动学（Post-Processed Kinematic, PPK）工作流进行处理，以实现厘米级的GPS定位精度。基准站数据：来自实时动态差分（Real-Time Kinematic, RTK）GPS固定观测站或可提供校正帧的静态定位设备。设备端GPS接收机：Emlid Reach M2型。数据质量分级：Q1占比94.26%，Q2占比4.82%，Q5占比0.92%。 ### 水深测量 本数据集采用ETC 400型单波束测深仪采集水深数据。我们仅保留已通过Q1级GPS校正的数据点，且仅纳入航迹航点处的测量值。同时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椭球为基准进行参考系转换，随后根据需求应用局部大地水准面模型进行修正。处理完成后，数据被投影至统一格网以生成栅格数据与Shapefile文件。格网单元尺寸为0.642米。栅格数据与Shapefile文件通过线性插值法生成，三维重建算法采用球枢算法（Ball Pivot Algorithm）。 ### 通用文件夹组织结构 数据集采用如下命名格式：YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number。 目录结构如下： ├── 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。若可对文件进行差分校正（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端数据；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，直接将文件放置于GPS目录根目录下。 │ ├── BASE：存储来自RTK观测站或静态定位设备的基准站数据。 │ └── DEVICE：存储来自采集设备的定位数据。 ├── METADATA：存储本次采集任务元数据信息文件的目录。 ├── PROCESSED_DATA：用于存储本次数据处理流程生成的所有结果文件的目录。 │ ├── BATHY：存储从任务日志中提取的原始水深测量数据的输出目录。 │ ├── FRAMES：存储从DCIM目录视频中提取的地理配准图像帧的输出目录。 │ ├── IA：用于存储图像识别预测结果的目标目录。 │ └── PHOTOGRAMMETRY：用于存储摄影测量重建模型的目标目录。 └── SENSORS：用于存储其他来源传感器文件的目录（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）。 ### 软件说明 所有原始数据均通过我们自研的工作流完成处理，所有预测结果均由我们的推理管线生成。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获取你的专属数据！