Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-07-26

This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-07-26. 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 53.62 GB of MP4 files, which were trimmed into 12087 frames (at 2997/1000 fps). The frames are georeferenced. 99.96% of these extracted images are useful and 0.04% 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: 95.97 %, Q2: 3.91 %, Q5: 0.12 % 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7月26日在留尼旺岛（Réunion）的赫尔米蒂奇（Hermitage）由自主水面载具（Autonomous Surface Vehicle）采集。 科学家或公民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领域。此类影像经标注后可用于训练人工智能（AI）模型，进而实现影像内目标的自动识别与预测。我们提供一套涵盖硬件与软件的工具集，用于海洋数据采集、物种或生境预测，以及测绘制图。 本数据集隶属于涵盖大量水下与航空影像的更大规模集合Seatizen Altas。相关研究方法、工具集与科学目标已在专门的数据论文中进行了详细阐述。 图像采集 本次采集会话共产生53.62 GB的MP4格式文件，经剪辑后得到12087帧图像（帧率为2997/1000 fps）。所有帧均已完成地理坐标标注。根据雅克模型（Jacques model）的预测结果，提取得到的图像中99.96%为有效帧，仅0.04%为无效帧。针对有效帧，我们已通过DinoVd'eau模型完成多标签预测。 GPS信息 GPS数据处理：本数据集采用后运动学差分（Post-Processed Kinematic, PPK）工作流进行处理，以实现厘米级的GPS定位精度。基站（Base）：数据来自rtk固定GPS基站或可提供校正帧的静态定位设备。设备GPS：采用Emlid Reach M2设备。数据质量分布：Q1级占比95.97%，Q2级占比3.91%，Q5级占比0.12%。 通用文件夹结构 通用文件夹命名格式：YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number。文件夹层级如下： ├── DCIM：用于存储采集所得的视频与照片文件，依媒体类型分类。 ├── GPS：用于存储所有与定位相关的文件。若可对文件进行校正（例如基于星历数据的后运动学差分处理），则需区分设备数据与基站数据；若仅存在设备位置数据且无法通过后处理技术校正（例如gpx格式文件），则无需区分基站与设备数据，直接将文件置于GPS文件夹根目录下。 │ ├── BASE：存储来自rtk基站或静态定位设备的文件。 │ └── DEVICE：存储来自采集设备的定位文件。 ├── METADATA：存储本次采集会话的通用信息文件。 ├── PROCESSED_DATA：用于存储本次会话数据处理所得的全部结果，包含以下子文件夹： │ ├── 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文件夹视频中提取的地理标注帧的输出目录。 │ ├── 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：存储其他来源的文件（例如回声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 所有原始数据均通过我们的标准化工作流进行处理，所有预测结果均由我们的推理流水线（inference pipeline）生成。你可在本仓库中获取下载该数据集所需的全部脚本。使用本数据集时请标注SeatizenDOI！