Underwater images collected by an Autonomous Surface Vehicle in Cap-Homard, Réunion - 2023-11-28

This dataset was collected by an Autonomous Surface Vehicle in Cap-Homard, Réunion - 2023-11-28.

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!

本数据集由自主水面航行器（Autonomous Surface Vehicle）于2023年11月28日在留尼旺岛卡奥马尔采集。

科研人员或社会公众采集的水下影像与航拍影像，可广泛应用于科学研究、资源管理与生态保护等领域。此类影像经标注后可共享，用于训练人工智能模型，以自动识别并预测影像中的目标对象。

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、物种或生境识别预测，以及测绘制图。

通用文件夹结构

通用文件夹命名格式：YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number
├── DCIM：用于存储采集所得视频与照片等各类媒体文件的文件夹。
├── GPS：用于存储各类定位相关文件的文件夹。若可对文件进行定位校正（例如基于RINEX格式数据的后处理运动学（Post-Processed Kinematic）校正），则需区分设备端数据与基准站数据；若仅存在设备端定位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端数据，直接将文件置于GPS文件夹根目录下。
│ ├── BASE：存储来自RTK基准站或其他静态定位仪器的文件。
│ └── DEVICE：存储来自采集设备的定位文件。
├── METADATA：存储本次任务会话通用信息文件的文件夹。
├── PROCESSED_DATA：用于存储本次会话数据处理结果的各级子文件夹，包含：
│ ├── BATHY：存储从任务日志中提取的测深原始数据的输出文件夹。
│ ├── FRAMES：存储从DCIM文件夹内视频中提取的地理参考帧的输出文件夹。
│ ├── IA：存储图像识别预测结果的目标文件夹。
│ └── PHOTOGRAMMETRY：存储摄影测量重建模型的目标文件夹。
└── SENSORS：用于存储其他来源文件的文件夹，例如回声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。

软件

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生成。您可在本仓库中获取下载该数据集所需的全部脚本。祝您使用本数据集顺利，可通过SeatizenDOI进行引用！