Underwater images collected by a Paddle in Hermitage, Réunion - 2021-02-18

This dataset was collected by a Paddle in Hermitage, Réunion - 2021-02-18. 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. 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2021年2月18日在留尼旺岛（Réunion）隐士湾（Hermitage）通过桨板采集获得。 科研人员与普通民众采集的水下及航拍图像，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（Artificial Intelligence）模型，以实现图像内目标的识别预测。我们提供一套软硬件工具集，可用于海洋数据采集、物种/生境预测及专题地图生成。 ### 通用文件夹结构 命名规则：YYYYMMDD_COUNTRYCODE-可选地点_设备_会话-编号 YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number ├── DCIM：用于存储采集所得各类媒体文件（视频与照片）的目录。 ├── GPS：用于存储各类定位相关文件的目录。若可对文件进行定位校正（例如基于RINEX格式数据实现后处理运动学（Post-Processed Kinematic, PPK）校正），则需区分设备端数据与基准站数据；若仅存在设备端定位数据且无法通过后处理技术校正（例如GPX格式文件），则无需区分基准站与设备端数据，直接将文件置于GPS目录根目录下。 │ ├── BASE：来自RTK基准站或静态定位仪器的文件。 │ └── DEVICE：来自采集设备的定位文件。 ├── METADATA：存储本次采集会话通用信息文件的目录。 ├── PROCESSED_DATA：用于存储本次会话数据处理全部结果的目录集合。 │ ├── BATHY：用于存放从任务日志中提取的水深原始数据的输出目录。 │ ├── FRAMES：用于存放从DCIM目录视频中提取的地理配准帧图像的输出目录。 │ ├── IA：用于存储图像识别预测结果的目标目录。 │ └── PHOTOGRAMMETRY：用于存储摄影测量重建模型的目标目录。 └── SENSORS：用于存储其他来源文件的目录（例如回声测深仪获取的水深数据、自动驾驶仪日志文件、任务规划文件等）。 ### 软件说明 本数据集全部原始数据均通过我们自研的工作流完成处理，所有预测结果均由我们的推理流水线生成。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。请尽情使用SeatizenDOI工具处理您的数据！