Underwater images collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-11-15

This dataset was collected by an Autonomous Surface Vehicle in Boucan, Réunion - 2023-11-15.

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.

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: 65.62 %, Q2: 27.99 %, Q5: 6.39 %

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 1.156 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!

本数据集由无人水面艇于留尼汪岛布坎地区采集，采集日期为2023年11月15日。

科学家与公民采集的水下或航拍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识别图像中的目标物体。本项目提供一套软硬件工具集，可用于海洋数据采集、物种/生境预测及地图绘制工作。

GPS信息：
本数据集采用后处理动态差分（PPK, Post-Processed Kinematic）流程进行处理，可实现厘米级GPS定位精度。
基准站数据：来自支持实时动态差分（RTK, Real-Time Kinematic）的固定GPS基准站，或可提供校正帧的静态定位设备的文件。
设备GPS模块：采用Emlid Reach M2型号设备。
数据质量分布：Q1占比65.62%，Q2占比27.99%，Q5占比6.39%。

水深测量数据：
水深测量数据采用单波束测深仪ETC 400采集。仅保留带有Q1级GPS校正的测深数值，仅保留航迹点对应的测量点数据，且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校准，随后根据需要应用当地大地水准面模型。数据处理完成后，将投影至统一格网以生成栅格数据与形状文件（Shapefile）。格网单元尺寸为1.156米。栅格与形状文件通过线性插值法生成，三维重建算法采用球支点算法（Ball Pivoting Algorithm）。

通用文件夹结构：
数据集采用如下命名规则与目录结构：YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number
├── DCIM：用于存储采集到的视频与照片等多媒体文件的目录。
├── 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（如基于RINEX数据的后处理动态差分）进行校正，则需区分设备数据与基准站数据；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，文件直接存放于GPS目录根目录下。
│ ├── BASE：存放来自RTK基准站或静态定位设备的文件。
│ └── DEVICE：存放来自采集设备的定位文件。
├── METADATA：存储本次任务的通用信息文件的目录。
├── PROCESSED_DATA：存储本次任务数据处理产出结果的目录，包含以下子目录：
│ ├── BATHY：存储从任务日志中提取的水深原始数据的输出目录。
│ ├── FRAMES：存储从DCIM目录下视频中提取的地理参考帧的输出目录。
│ ├── 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：存储其他来源文件的目录，包括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。

软件说明：
所有原始数据均通过本团队自研流程进行处理，所有预测结果均由本团队的推理管线生成。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。祝您使用本数据集顺利，可通过SeatizenDOI引用该数据集！