Underwater images collected by an Autonomous Surface Vehicle in Tessier, Réunion - 2024-04-05

This dataset was collected by an Autonomous Surface Vehicle in Tessier, Réunion - 2024-04-05.

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 30.54 GB of MP4 files, which were trimmed into 11362 frames (at 2997/1000 fps).
The frames are georeferenced.
99.65% of these extracted images are useful and 0.35% 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: 93.52 %, Q2: 5.31 %, Q5: 1.17 %

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 2.0 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 0.137 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2024年4月5日在留尼旺岛泰西耶（Tessier）采集。

科学家或民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。此类影像可经标注后共享，用于训练人工智能模型以实现影像目标识别。我们提供一套软硬件工具集，可用于海洋数据采集、物种/栖息地识别及地图绘制。

### 影像采集
本次采集任务包含30.54GB的MP4视频文件，经剪辑后提取出11362帧影像（帧率为2997/1000 fps）。所有帧均已完成地理配准。经Jacques模型预测，其中99.65%的提取影像为有效数据，剩余0.35%为无效数据。针对有效帧，我们已使用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。
- 基准站文件：来自实时动态差分（Real-time Kinematic, RTK）GPS固定基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。
数据质量分级：Q1占比93.52%，Q2占比5.31%，Q5占比1.17%。

### 水深测量
本次数据采用单波束回声测深仪ETC 400采集。我们仅保留带有Q1级GPS校正的测点数据以及航迹点数据；同时筛选出水深估算值介于0.2米至2.0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，若存在局部大地水准面则进行校正。处理完成后，数据被投影至均匀网格以生成栅格文件与Shapefile矢量文件，网格单元尺寸为0.137米。栅格与矢量文件通过线性插值生成，三维重建算法采用球枢轴算法（ballpivot）。

### 标准文件夹结构
命名格式：`YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number`
- `DCIM`：用于存储采集到的视频与照片文件的目录。
- `GPS`：存储所有与定位相关文件的目录。若文件可通过后处理技术进行校正（例如基于RINEX（Receiver Independent Exchange Format）数据的后处理运动学校正），则需区分设备数据与基准站数据，此时目录下将包含`BASE`与`DEVICE`子目录：
- `BASE`：来自RTK基站或静态定位设备的基准站文件
- `DEVICE`：来自采集设备的定位数据文件
若仅存在设备定位数据且无法通过后处理技术校正（例如GPX（GPS Exchange Format）文件），则无需区分基准站与设备数据，所有文件直接放置于`GPS`目录根目录下。
- `METADATA`：存储本次采集任务通用信息文件的目录。
- `PROCESSED_DATA`：存储本次任务数据处理结果的目录，包含以下子文件夹：
- `BATHY`：从任务日志中提取的水深原始数据输出目录
- `FRAMES`：从`DCIM`目录下的视频中提取的地理配准帧输出目录
- `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`：存储其他来源文件的目录，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我们自研的工作流进行处理，所有预测结果均由我们的推理管线生成。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SeatizenDOI开展研究顺利！