Underwater images collected by an Autonomous Surface Vehicle in St-Leu, Réunion - 2024-03-14

This dataset was collected by an Autonomous Surface Vehicle in St-Leu, Réunion - 2024-03-14. 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 35.3 GB of MP4 files, which were trimmed into 12827 frames (at 2997/1000 fps). The frames are georeferenced. 99.56% of these extracted images are useful and 0.44% 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: 99.22 %, Q2: 0.69 %, Q5: 0.08 % 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 10.0 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 0.21 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（Autonomous Surface Vehicle）于2024年3月14日在留尼汪岛圣勒采集。 科学家或民众采集的水下、航拍图像可广泛应用于科学研究、资源管理与生态保护领域。此类图像经标注后可共享，用于训练人工智能（AI）模型以实现图像内目标的自动识别。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/栖息地识别及地图生成。 本数据集隶属于涵盖大量水下与航拍图像的Seatizen Atlas大型数据集集合。相关研究方法、工具及科学目标已在专门的数据论文中详述。 图像采集 本次采集任务共生成35.3 GB的MP4视频文件，经剪辑后提取得到12827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，其中99.56%的图像为有效帧，剩余0.44%为无效帧。针对有效帧，我们已通过DinoVd'eau模型完成多标签标注预测。 GPS信息 本数据采用后处理动态差分（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：来自实时动态差分（RTK, Real-Time Kinematic）固定GPS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。 数据质量分布：Q1占比99.22%，Q2占比0.69%，Q5占比0.08%。 测深数据 本数据集采用单波束测深仪ETC 400采集。我们仅保留符合Q1级GPS校正条件的测深值，同时保留航迹点数据，并筛选出水深估算值介于0.2米至10.0米之间的原始数据。 数据处理流程如下：首先以WGS84椭球为基准进行参考系对齐，若存在局部大地水准面则进行校正；最终将数据投影至统一网格以生成栅格文件与形状文件（shapefiles）。网格单元尺寸为0.21米，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 采用如下命名格式：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：用于存储所有定位相关文件。若可对文件进行差分校正（如基于RINEX数据的后处理动态差分），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校正（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：来自RTK基准站或静态定位设备的文件。 │ └── DEVICE：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：用于存储本次采集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测深原始数据输出目录。 │ ├── FRAMES：从DCIM目录视频中提取的地理配准帧图像输出目录。 │ ├── IA：图像识别预测结果的存储目录。 │ └── PHOTOGRAMMETRY：摄影测量重建模型的存储目录。 └── SENSORS：用于存储其他传感器源文件的目录（如测深仪采集的测深数据、自动驾驶仪日志文件、任务规划文件等）。 软件工具链 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。你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开展研究顺利！