Underwater images collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-06

This dataset was collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-06. 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 82.86 GB of MP4 files, which were trimmed into 18809 frames (at 2997/1000 fps). The frames are georeferenced. 69.62% of these extracted images are useful and 30.38% 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: 55.29 %, Q2: 1.04 %, Q5: 43.67 % 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 0.652 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6日由留尼汪岛布坎地区的自主水面航行器（Autonomous Surface Vehicle）采集。 科研人员或公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。此类影像经标注后可共享，用于训练人工智能模型以实现图像目标预测。我们提供一套涵盖硬件与软件的工具集，可用于海洋数据采集、物种或栖息地识别以及地图生成。 本数据集隶属于收录海量水下与航空影像的大型合集Seatizen Atlas。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述。 ### 图像采集 本次采集任务生成总计82.86 GB的MP4视频文件，经剪辑后提取出18809帧图像，帧率为2997/1000 fps（约2.997 fps）。所有提取帧均已完成地理配准。经Jacques模型预测，其中69.62%的提取图像为有效样本，剩余30.38%为无效样本。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，以实现厘米级GPS定位精度。 - 基准站数据：来自RTK GPS固定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。 本数据集的质量分级占比为：Q1 55.29%，Q2 1.04%，Q5 43.67%。 ### 水深测深数据 本次数据采用ETC 400单波束测深仪采集。仅保留带有Q1级GPS校正的测点数据，同时保留航迹点数据。仅保留水深估算值介于0.2 m至50.0 m之间的原始测深数据。 数据首先以WGS84椭球面为基准进行参考系对齐，随后根据需要叠加本地大地水准面模型。处理完成后，数据将被投影至统一格网以生成栅格文件与形状文件（Shapefiles），格网单元尺寸为0.652 m。栅格与形状文件通过线性插值生成，三维重建算法采用球枢算法（ballpivot）。 ### 通用文件夹命名与结构 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文件的目录。 ├── GPS：用于存储各类定位相关文件。若文件可通过后处理技术（如基于RINEX数据的后处理动态差分）进行校正，则需区分基准站数据与设备数据；若仅存在设备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数据，文件直接存放于GPS目录根目录下。 │ ├── BASE：来自RTK基站或静态定位设备的基准站数据文件。 │ └── DEVICE：来自采集设备的定位数据文件。 ├── 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，包含以下子目录： │ ├── BATHY：从任务日志中提取的原始测深数据输出目录。 │ ├── FRAMES：从DCIM目录下的视频中提取的地理配准帧图像输出目录。 │ ├── IA：图像识别预测结果存储目录。 │ └── PHOTOGRAMMETRY：摄影测量重建模型存储目录。 └── 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。 ### 软件说明 所有原始数据均通过自研工作流完成处理，所有预测结果均由自研推理管线生成。本数据集的下载脚本可在对应代码仓库中获取。祝您使用SeatizenDOI数据集顺利！