Underwater images collected by a Kite surf in Le-Morne, Mauritius - 2019-09-24

This dataset was collected by a Kite surf in Le-Morne, Mauritius - 2019-09-24. 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. This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. Methods, tools and scientific objectives are also described in a dedicated data paper. GPS information: No GPS. 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2019年9月24日在毛里求斯勒莫尔半岛（Le-Morne）由一名风筝冲浪者采集。 科学家与民众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（AI）模型，进而实现图像内目标的自动识别与预测。 我们提供一套涵盖硬件与软件的工具集，可用于海洋数据采集、物种或栖息地识别，并可生成相关专题地图。 本数据集隶属于涵盖大量水下与航拍图像的大型集合Seatizen 图集（Seatizen Atlas）。相关方法、工具及本数据集的科学目标，已在专属数据论文中详细阐述。 GPS信息：无GPS定位数据。 通用文件夹命名与结构 命名格式为：YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number ├── DCIM：用于存储采集到的各类视频与照片的文件夹。 ├── GPS：用于存储各类定位相关文件的文件夹。若可对文件进行校正（例如基于RINEX数据开展后处理运动学解算），则需区分基站数据与设备端数据；若仅存在设备端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，直接将文件存放于GPS文件夹根目录。 │ ├── BASE：存放来自RTK基站或其他静态定位仪器的文件。 │ └── DEVICE：存放来自采集设备的文件。 ├── 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各级子文件夹，包含： │ ├── BATHY：从任务日志中提取的测深原始数据输出文件夹。 │ ├── FRAMES：从DCIM文件夹内的视频中提取的带地理参考帧的输出文件夹。 │ ├── 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。 软件处理 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。您可在本代码仓库中获取下载该数据集所需的全部脚本。祝您使用SeatizenDOI开展研究顺利！