Underwater images collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-11-24

This dataset was collected by an Autonomous Surface Vehicle in Hermitage, Réunion - 2023-11-24.

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 31.04 GB of MP4 files, which were trimmed into 11523 frames (at 2997/1000 fps).
The frames are georeferenced.
98.7% of these extracted images are useful and 1.3% 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: 98.93 %, Q2: 1.02 %, Q5: 0.05 %

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 0.538 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!

本数据集于2023年11月24日在留尼汪岛（Réunion）的赫尔米蒂奇（Hermitage）由自主水面载具（Autonomous Surface Vehicle）采集。
科学家或公民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领域。此类图像可经标注后共享，用于训练人工智能模型，以实现图像内目标物体的识别预测。我们提供一套软硬件工具集，用于海洋数据采集、物种/生境预测及地图生成。

图像采集
本次采集任务共生成31.04 GB的MP4视频文件，经剪辑后提取出11523帧画面（帧率为2997/1000 fps）。
所有帧均已完成地理配准（georeferenced）。
依据雅克模型（Jacques model）的预测结果，提取出的图像中98.7%为有效帧，剩余1.3%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GPS定位精度。
基准站数据：来自rtk固定GPS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。
数据质量分布：Q1占比98.93%，Q2占比1.02%，Q5占比0.05%。

测深数据
本次数据采用单波束测深仪（single-beam echosounder）S500采集。
我们仅保留Q1质量等级且带有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 m至50.0 m之间的原始数据。
数据首先以WGS84椭球面（WGS84 ellipsoid）为基准进行坐标参考系转换，随后若存在本地大地水准面，则应用本地大地水准面校正。
处理完成后，数据被投影至统一格网以生成栅格文件（raster）与形状文件（shapefiles）。
格网单元尺寸为0.538 m。
栅格文件与形状文件通过线性插值生成，三维重建算法采用ballpivot算法。

通用文件夹结构
命名格式：YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number
├── DCIM：用于存储采集到的视频与照片等媒体文件的文件夹。
├── GPS：用于存储所有与定位相关的文件。若可对文件进行定位校正（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数据；若仅存在设备位置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：来自rtk基准站或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测深原始数据的输出文件夹。
│ ├── FRAMES：从DCIM文件夹的视频中提取的地理配准帧的输出文件夹。
│ ├── 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。你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提供的数据顺利！