Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-09-25

This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-09-25.

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.

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25日在留尼旺岛（Réunion）的赫尔米特奇（Hermitage）由自主水面载具（Autonomous Surface Vehicle）采集。

科学家或民众采集的水下或航空图像可广泛应用于科学研究、资源管理与生态保护。此类图像经标注后可用于训练人工智能（Artificial Intelligence）模型，进而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、物种或生境识别，以及地图生成。

### 通用文件夹结构
文件夹命名格式为：YYYYMMDD_COUNTRYCODE-[可选地点]_设备_任务_编号
├── DCIM：用于存储采集所得各类视频与照片的文件夹
├── GPS：用于存储所有定位相关文件的文件夹。若可对文件进行位置校正（例如基于RINEX格式观测数据（rinex data）开展后处理动态定位（Post-Processed Kinematic）），则需区分设备原始数据与基准站数据；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），则无需区分基准站与设备数据，直接将文件置于GPS文件夹根目录。该文件夹包含子目录：
│ ├── BASE：来自RTK基站或其他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文件夹
├── PROCESSED_DATA：用于存储本次任务数据处理结果的子文件夹集合，包含：
│ ├── BATHY：从任务日志中提取的水深原始数据的输出目录
│ ├── FRAMES：从DCIM文件夹内的视频中提取的带地理参考信息的帧的输出目录
│ ├── 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：用于存储其他来源文件的文件夹，例如回声测深仪采集的水深数据、自动驾驶仪日志文件、任务规划文件等。

### 软件说明
本数据集所有原始数据均通过我们的工作流完成处理，所有预测结果均由我们的推理管线（inference pipeline）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顺利开展相关研究！