Underwater images collected by an Autonomous Surface Vehicle in Trou-Deau, Réunion - 2024-05-17

This dataset was collected by an Autonomous Surface Vehicle in Trou-Deau, Réunion - 2024-05-17.

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 30.21 GB of MP4 files, which were trimmed into 10759 frames (at 2997/1000 fps).
The frames are georeferenced.
99.8% of these extracted images are useful and 0.2% 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: 85.01 %, Q2: 6.69 %, Q5: 8.3 %

Bathymetry

The data are collected using a single-beam echosounder ETC 400.

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 0.12 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载具（Autonomous Surface Vehicle）于留尼旺岛Trou-Deau海域采集，采集日期为2024年5月17日。

由科研人员或公众采集的水下、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（AI）模型，以实现影像内目标的自动识别。本套件包含硬件与软件工具，可用于海洋数据采集、物种/生境预测以及地图生成。

## 图像采集
本次采集任务包含总容量30.21GB的MP4视频文件，经剪辑后提取得到10759帧图像（帧率为2997/1000 fps）。所有提取帧均已完成地理配准。经Jacques模型预测，其中99.8%的提取图像为有效帧，剩余0.2%为无效帧。针对有效帧，本团队使用DinoVd'eau模型完成了多标签分类预测。

## GPS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GPS定位精度。基站文件：来自RTK（Real-Time Kinematic）固定GPS基站或可提供校正帧的静态定位设备的相关文件。设备GPS采用Emlid Reach M2模块。本数据集质量分布：Q1占比85.01%，Q2占比6.69%，Q5占比8.3%。

## 测深数据
本数据集采用单波束测深仪ETC 400采集。仅保留带有Q1级GPS校正信息的测深值，同时保留航点数据。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对齐，若存在本地大地水准面则进一步应用其进行校正。处理完成后，数据被投影至均匀网格中，生成栅格文件与Shapefile文件。网格单元尺寸为0.12米，栅格与矢量文件均通过线性插值生成，三维重建算法采用球枢（ballpivot）算法。

## 通用文件夹结构
命名格式为YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number，具体目录结构如下：
├── DCIM：用于存储采集所得的视频与照片文件，依媒体类型分类存放。
├── GPS：用于存储所有与定位相关的文件。若文件可通过后处理技术进行校正（例如基于RINEX数据的后处理运动学校正），则需区分基站数据与设备数据；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），则无需区分基站与设备数据，文件直接存放于GPS文件夹根目录。
│ ├── BASE：来自RTK基站或静态定位设备的相关文件。
│ └── DEVICE：来自采集设备的定位相关文件。
├── METADATA：用于存储本次采集任务的通用信息文件。
├── PROCESSED_DATA：用于存储本次任务数据处理所得全部结果的目录，下设子文件夹：
│ ├── BATHY：从任务日志中提取的测深原始数据输出目录。
│ ├── FRAMES：从DCIM文件夹视频中提取的地理配准帧图像输出目录。
│ ├── IA：图像识别预测结果存储目录。
│ └── PHOTOGRAMMETRY：摄影测量重建模型存储目录。
└── SENSORS：用于存储其他来源的文件，例如测深仪采集的测深数据、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流水线生成。您可在本代码仓库中获取下载该数据集所需的全部脚本文件。祝您使用本数据集愉快，可通过SeatizenDOI进行引用！