Underwater images collected by Scuba diving in Hermitage, Réunion - 2021-03-17

This dataset was collected by Scuba diving in Hermitage, Réunion - 2021-03-17. 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 8.82 GB of MP4 files, which were trimmed into 1540 frames (at 1 fps). The frames are not georeferenced. 80.71% of these extracted images are useful and 19.29% are useless, according to predictions made by Jacques model. Multilabel predictions have been made on useful frames using DinoVd'eau model. GPS information: Base : No Base Device GPS : Emlid Reach M2 Quality of our data - Q1: 0 %, Q2: 0 %, Q5: 0 % 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!本数据集采集于2021年3月17日留尼旺岛（Réunion）隐士港（Hermitage）的水肺潜水作业期间。 科学家与公民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领域。此类影像经标注与共享后，可用于训练人工智能（AI）模型，以实现影像内目标的自动识别预测。我们提供了一套涵盖硬件与软件的工具集，可用于海洋数据采集、物种/生境识别预测，并辅助生成专题地图。 图像采集 本次采集的原始数据为8.82 GB的MP4视频文件，经剪辑并以1帧/秒的帧率抽帧后，共得到1540帧图像。 所有抽帧图像均未进行地理配准。 根据雅克（Jacques）模型的预测结果，其中80.71%的抽帧图像为有效样本，剩余19.29%为无效样本。 针对有效样本帧，已通过DinoVd'eau模型完成多标签标注预测。 GPS信息 基准站：无基准站 采集设备GPS型号：Emlid Reach M2 数据质量分级：Q1占比0%，Q2占比0%，Q5占比0% 通用文件夹组织结构 本数据集采用如下通用命名与组织规范： YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number ├── DCIM：用于存储采集所得的视频与照片文件的目录。 ├── GPS：用于存储各类定位相关文件的目录。若可通过基于RINEX数据的后处理运动学（Post-Processed Kinematic）方法对定位文件进行校正，则需区分基准站数据与设备端数据，分别存储于子目录中；若仅存在设备端定位数据且无法通过后处理技术校正（如GPX格式文件），则无需区分基准站与设备数据，直接将文件置于GPS目录根路径下。 │ ├── BASE：存储来自RTK基准站或静态定位仪器的文件。 │ └── DEVICE：存储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目录。 ├── PROCESSED_DATA：用于存储本次采集任务数据处理结果的目录，包含以下子目录： │ ├── BATHY：存储从任务日志中提取的测深原始数据的输出目录。 │ ├── FRAMES：存储从DCIM目录下视频中抽取出的已配准帧图像的输出目录。 │ ├── 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：用于存储其他来源传感器文件的目录，例如回声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。 软件处理说明 本次所有原始数据均通过我们的标准化处理工作流完成处理，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文件。祝您使用SeatizenDOI提供的数据集顺利！