Underwater images collected by an Autonomous Surface Vehicle in Grandfond, Réunion - 2023-01-18

This dataset was collected by an Autonomous Surface Vehicle in Grandfond, Réunion - 2023-01-18. 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 9.5 GB of MP4 files, which were trimmed into 4234 frames (at 2997/1000 fps). The frames are georeferenced. 97.0% of these extracted images are useful and 3.0% 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: 86.54 %, Q2: 10.59 %, Q5: 2.87 % 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 5.0 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 0.337 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1月18日在留尼汪岛的Grandfond海域由自主水面航行器（Autonomous Surface Vehicle）采集。 科研人员或民众采集的水下、航空影像可广泛应用于科学研究、资源管理与生态保护。这类影像可经标注后用于训练人工智能（AI）模型，以实现影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、物种或生境预测以及地图生成。 本数据集是包含大量水下与航空影像的Seatizen Altas大型数据集集合的一部分。相关方法、工具与科学目标已在专门的数据论文中进行了详述。 ### 影像采集 本次采集任务产生了总容量9.5GB的MP4文件，经剪辑后得到4234帧影像（帧率为2997/1000 fps）。所有帧均已完成地理配准。根据雅克模型（Jacques model）的预测结果，其中97.0%的提取影像可用，剩余3.0%无效。针对可用帧，我们已使用DinoVd'eau模型完成了多标签预测。 ### GPS信息 本数据采用后处理运动学（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（Base）：来自RTK GPS固定观测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。 本数据质量分级：Q1占比86.54%，Q2占比10.59%，Q5占比2.87% ### 水深测量 本数据使用单波束回声测深仪（echosounder）ETC 400采集。 我们仅保留了Q1等级且带有GPS校正的测点数值，同时保留作为航点的测点数据。我们还保留了水深估算值介于0.2米至5.0米之间的原始数据。 数据首先以WGS84椭球（WGS84 ellipsoid）为基准进行参考系匹配，随后在可用时使用本地大地水准面（geoid）进行校正。处理完成后，数据被投影至统一格网以生成栅格数据（raster）与矢量形状文件（shapefiles）。格网单元尺寸为0.337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轴（ballpivot）算法。 ### 通用文件夹结构 命名格式：`YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number` ├── DCIM：用于存储采集到的视频与照片的文件夹。 ├── 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数据的后处理运动学校正），则需区分基准站数据与设备数据；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，文件直接放置于GPS文件夹根目录。 │ ├── BASE：来自RTK观测站或静态定位设备的文件。 │ └── DEVICE：来自采集设备的文件。 ├── METADATA：包含本次任务通用信息文件的文件夹。 ├── PROCESSED_DATA：用于存储当前任务数据处理结果的所有子文件夹。 │ ├── BATHY：从任务日志中提取的水深原始数据的输出文件夹。 │ ├── FRAMES：从DCIM视频中提取的已地理配准影像帧的输出文件夹。 │ ├── IA：图像识别预测结果的目标文件夹。 │ └── PHOTOGRAMMETRY：摄影测量（photogrammetry）重建模型的目标文件夹。 └── SENSORS：用于存储其他来源的文件（例如回声测深仪采集的水深数据、自动驾驶仪的日志文件、任务规划文件等）。 ### 软件说明 所有原始数据均通过我们的工作流进行处理，所有预测结果均由我们的推理流水线（inference pipeline）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。欢迎使用SeatizenDOI开展相关研究！