Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-12-05

This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-12-05.

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 26.3 GB of MP4 files, which were trimmed into 9348 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: 95.62 %, Q2: 4.17 %, Q5: 0.21 %

Bathymetry

The data are collected using a single-beam echosounder S500.

We only keep the values which have a GPS correction in Q1.
We keep the points that are the waypoints.

We keep the raw data where depth was estimated between 0.2 m and 50.0 m deep.
The data are first referenced against the WGS84 ellipsoid. Then we apply the local geoid if available.
At the end of processing, the data are projected into a homogeneous grid to create a raster and a shapefiles.
The size of the grid cells is 0.123 m.
The raster and shapefiles are generated by linear interpolation. The 3D reconstruction algorithm is ballpivot.

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）于留尼旺岛（Réunion）赫尔米塔奇地区采集，采集日期为2023年12月5日。

科学家或民众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。此类影像经标注与共享后，可用于训练人工智能模型以实现影像内目标的识别预测。本项目提供一套包含硬件与软件的工具集，可用于海洋数据采集、物种/栖息地识别以及地图生成。

### 图像采集
本次采集任务包含26.3 GB的MP4视频文件，经剪辑后提取得到9348帧图像（帧率为2997/1000 fps，即约2.997 fps）。所有提取帧均已完成地理配准。根据雅克（Jacques）模型的预测结果，本次提取的图像中99.97%为有效帧，0.03%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运动学（Post-Processed Kinematic, PPK）工作流进行处理，以实现厘米级的GPS定位精度。
- 基准站数据：来自实时动态差分（RTK）GPS固定站或可提供校正帧的静态定位设备的文件
- 设备GPS：采用Emlid Reach M2设备
本次数据质量分布：Q1级占比95.62%，Q2级占比4.17%，Q5级占比0.21%

### 测深数据
本次测深数据采用单波束测深仪S500采集。仅保留带有Q1级GPS校正的测点数据，同时保留航迹点信息。仅保留水深估算值介于0.2米至50.0米之间的原始数据。
数据首先以WGS84椭球面为基准进行参考定位，若存在本地大地水准面，则进一步应用本地大地水准面校正。处理完成后，将数据投影至统一格网，生成栅格文件与矢量形状文件。格网单元尺寸为0.123米，栅格与形状文件通过线性插值生成。本次三维重建采用球枢（ballpivot）算法。

### 标准文件夹结构
文件夹命名格式为：YYYYMMDD_COUNTRYCODE-可选地点_设备_任务-编号
目录结构如下：
├── DCIM/：用于存储采集到的视频与照片文件，根据采集介质类型分类
├── GPS/：存储所有与定位相关的文件。若文件可通过后处理技术（如基于RINEX数据的后处理运动学校正）进行定位校正，则将基准站数据与设备数据分别存储；若仅存在设备定位数据且无法通过后处理校正（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/：来自RTK基准站或静态定位设备的基准站数据
│ └── DEVICE/：来自采集设备的定位数据
├── METADATA/：存储本次采集任务的通用信息文件
├── PROCESSED_DATA/：存储本次采集任务数据处理后的所有结果文件
│ ├── BATHY/：从任务日志中提取的测深原始数据输出目录
│ ├── FRAMES/：从DCIM视频中提取的地理配准帧图像输出目录
│ ├── 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/：存储其他来源的文件（如测深仪采集的测深数据、自动驾驶仪日志、任务规划文件等）

### 软件工具
所有原始数据均通过本项目开发的工作流进行处理，所有预测结果均由本项目的推理流水线生成。你可在本代码仓库中获取下载该数据集所需的全部脚本文件。祝您使用本数据集愉快，可通过SeatizenDOI引用本数据集！