Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-05-31

This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-05-31. 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 30.32 GB of MP4 files, which were trimmed into 10244 frames (at 2997/1000 fps). The frames are georeferenced. 86.22% of these extracted images are useful and 13.78% 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: 24.24 %, Q2: 71.19 %, Q5: 4.57 % 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!本数据集由无人水面航行器（Autonomous Surface Vehicle）于2023年5月31日在留尼旺岛（Réunion）的赫尔米塔奇（Hermitage）海域采集。 科学家或民众采集的水下与航拍影像可广泛应用于科学研究、资源管理与生态保护领域。可对这些影像进行标注并共享，用于训练人工智能（Artificial Intelligence）模型，进而实现影像内目标的识别预测。本团队提供一套软硬件工具集，可用于海洋数据采集、物种/栖息地识别预测以及地图生成。 本数据集是大型数据集项目Seatizen Altas的一部分，该项目收录了大量水下与航拍影像。相关方法、工具及科学目标已在专属数据集论文中详述。 ### 影像采集 本次采集任务包含30.32 GB的MP4视频文件，经剪辑后得到10244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，提取得到的影像中86.22%为有效帧，13.78%为无效帧。针对有效帧，已通过DinoVd'eau模型完成多标签预测。 ### GPS信息 本数据集采用后处理动态定位（Post-Processed Kinematic, PPK）工作流进行处理，以实现厘米级GPS定位精度。 基准站数据：来自实时动态差分（Real-time Kinematic, RTK）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：Emlid Reach M2 数据质量分级：Q1占比24.24%，Q2占比71.19%，Q5占比4.57% ### 标准文件夹结构 采用`YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number`命名格式，具体子文件夹如下： - `DCIM`：存储采集得到的视频与照片文件的文件夹。 - `GPS`：存储所有与定位相关的文件。若可对文件进行后处理校正（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；若仅存在设备位置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，直接将文件放置在`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`：存储本次采集任务通用信息文件的文件夹。 - `PROCESSED_DATA`：存储本次任务数据处理结果的文件夹，包含以下子目录： - `BATHY`：从任务日志中提取的水深原始数据输出文件夹。 - `FRAMES`：从`DCIM`视频中提取的地理配准帧输出文件夹。 - `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`：存储其他来源文件的文件夹（例如回声测深仪获取的水深数据、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本团队开发的工作流进行处理，所有预测结果均由本团队的推理管线生成。您可在本代码仓库中获取下载该数据集所需的全部脚本。祝您借助SeatizenDOI顺利开展相关研究工作！