Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-06-01

This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-06-01.

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.

Image acquisition
This session has 27.55 GB of MP4 files, which were trimmed into 9455 frames (at 2997/1000 fps).
The frames are georeferenced.
99.97% of these extracted images are useful and 0.03% 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: 76.2 %, Q2: 20.09 %, Q5: 3.71 %

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载具（Autonomous Surface Vehicle）于2023年6月1日在留尼旺岛（Réunion）的赫尔米特奇地区采集。

科学家或公众采集的水下或航空影像，可广泛应用于科学研究、资源管理与生态保护领域。此类影像可经标注后共享，用于训练人工智能模型，进而实现影像内目标的自动识别与预测。本套件提供涵盖硬件与软件的全套工具，可用于海洋数据采集、物种或生境识别预测，以及地图生成。

影像采集
本次采集任务共生成27.55GB的MP4文件，经裁切后得到9455帧图像（帧率为2997/1000 fps）。所有帧均已完成地理配准（georeferenced）。根据雅克模型（Jacques model）的预测结果，提取的图像中99.97%为有效样本，仅0.03%为无效样本。针对有效帧，已通过DinoVd'eau模型完成多标签预测。

GPS定位信息
本数据集采用后处理运动学（PPK, Post-Processed Kinematic）工作流进行处理，以实现厘米级的GPS定位精度。
基准站数据：来自RTK GPS固定观测站或可提供校正帧的静态定位设备。
设备GPS模块：采用Emlid Reach M2设备。
数据质量分布：Q1占比76.2%，Q2占比20.09%，Q5占比3.71%

通用文件夹组织结构
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：用于存储所有定位相关文件。若可对文件进行差分校正（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端数据，分别存储于对应子目录；若仅存在设备端定位数据且无法通过后处理技术进行校正（例如GPX文件），则无需区分基准站与设备数据，直接将文件置于GPS文件夹根目录。
│ ├── BASE：存储来自RTK观测站或静态定位设备的基准站数据。
│ └── DEVICE：存储来自采集设备的定位数据。
├── 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目录，包含以下子文件夹：
│ ├── BATHY：存储从任务日志中提取的水深测量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配准帧的输出目录。
│ ├── 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：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。

软件工具
所有原始数据均通过本团队开发的工作流完成处理，所有预测结果均由本团队的推理管线生成。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SeatizenDOI开展科研工作顺利！