Underwater images collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-08

This dataset was collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-09-08. 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 77.52 GB of MP4 files, which were trimmed into 17379 frames (at 2997/1000 fps). The frames are georeferenced. 67.48% of these extracted images are useful and 32.52% 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: 76.69 %, Q2: 2.36 %, Q5: 20.96 % 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 0.521 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8日由留尼旺岛布坎地区的自主水面载具（Autonomous Surface Vehicle）采集。 科学家与民众采集的水下及航空影像，可广泛应用于科学研究、资源管理与生态保护领域。此类影像经标注并共享后，可用于训练人工智能（AI, Artificial Intelligence）模型以实现影像内目标物的自动识别。我们提供一套包含硬件与软件的工具集，用于海洋数据采集、物种或栖息地识别以及地图生成。 本数据集隶属于涵盖海量水下与航空影像的大型集合Seatizen Altas，其采集方法、配套工具与科学目标已在专属数据论文中详细阐释。 ### 图像采集 本次采集任务共生成77.52 GB的MP4视频文件，经剪辑后提取得到17379帧图像，帧率为2997/1000 fps（约2.997帧/秒）。所有提取得到的图像均已完成地理配准。经Jacques模型预测，其中67.48%的图像为有效帧，剩余32.52%为无效帧。针对有效帧，我们采用DinoVd'eau模型完成了多标签分类预测。 ### GPS信息 本数据集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RTK（实时动态差分）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76.69%，Q2占比2.36%，Q5占比20.96%。 ### 测深数据 本次测深采用单波束测深仪（single-beam echosounder）ETC 400完成。我们仅保留符合以下条件的测深数据： 1. 带有Q1级GPS校正信息的数据； 2. 属于航点路径的测点； 3. 水深估计值介于0.2 m至50.0 m之间的原始数据。 数据处理流程如下：首先以WGS84椭球面（WGS84 ellipsoid）为基准进行参考对齐，随后根据需要使用局部大地水准面（geoid）进行修正。处理完成后，将数据投影至统一网格以生成栅格文件（raster）与形状文件（shapefiles）。网格单元尺寸为0.521 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）。 ### 通用文件夹结构 文件夹命名规则：`YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number` ├── DCIM：用于存储采集得到的视频与照片文件。 ├── GPS：用于存储所有与定位相关的文件。若可对文件进行校正（例如基于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，文件直接存放于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：用于存储本次任务数据处理结果的文件夹，包含以下子目录： │ ├── BATHY：存储从任务日志中提取的测深原始数据的输出目录。 │ ├── FRAMES：存储从DCIM视频中提取的地理配准图像帧的输出目录。 │ ├── IA：存储图像识别预测结果的目标目录。 │ └── PHOTOGRAMMETRY：存储摄影测量重建模型的目标目录。 └── SENSORS：存储其他来源文件的文件夹，例如测深仪获取的测深数据、自动驾驶仪日志文件、任务规划文件等。 ### 配套软件 所有原始数据均通过我们的处理工作流完成处理，所有预测结果均由我们的推理管线生成。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顺利！