Underwater images collected by an Autonomous Surface Vehicle in St-Brandon, Mauritius - 2022-11-20

This dataset was collected by an Autonomous Surface Vehicle in St-Brandon, Mauritius - 2022-11-20. 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. This session has 26.36 GB of MP4 files, but no images were trimmed. GPS information: Base : No Base Device GPS : Emlid Reach M2 Quality of our data - Q1: 0.0 %, Q2: 0.0 %, Q5: 100.0 % Bathymetry The data are collected using a single-beam echosounder S500. We keep the points that are the waypoints. We keep the raw data where depth was estimated between 4.0 m and 40.0 m deep. The data are first referenced against the WGS84 ellipsoid. 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 1.234 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!本数据集于2022年11月20日由自主水面航行器（Autonomous Surface Vehicle）在毛里求斯圣布兰登采集。 科学家或民众采集的水下与航拍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。我们提供一套涵盖硬件与软件的工具集，可用于海洋数据采集、物种/生境预测及地图生成。 本数据集隶属于涵盖海量水下与航拍影像的大型数据集集合Seatizen Atlas。相关方法、工具及科学目标已在专属数据论文中详述。 本次采集会话包含26.36 GB的MP4格式文件，未对影像进行剪辑。 GPS相关信息： 基准站：无基准站 设备GPS：Emlid Reach M2 数据质量：Q1为0.0%，Q2为0.0%，Q5为100.0% 测深数据 本次数据采用单波束回声测深仪S500采集。 我们仅保留航迹点对应的测点，以及水深介于4.0米至40.0米之间的原始测深数据。 数据首先以WGS84椭球面为基准进行坐标参考系对齐。 处理完成后，数据被投影至统一格网以生成栅格（raster）与形状文件（shapefiles）。 格网单元尺寸为1.234米，栅格与形状文件通过线性插值生成。三维重建算法采用球枢算法（ballpivot）。 通用文件夹命名与结构 命名格式：YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number ├── DCIM：用于存储采集所得视频与照片的文件夹，依采集介质类型区分。 ├── GPS：用于存储所有定位相关文件的文件夹。若可对文件进行位置校正（例如基于星历数据的后运动学差分处理），则需将基准站数据与设备数据分别存放；若仅存在设备位置数据且无法通过后处理技术校正（例如GPX文件），则无需区分基准站与设备数据，直接将文件存放于GPS文件夹根目录。 │ ├── BASE：存放来自RTK基准站或静态定位仪器的文件。 │ └── DEVICE：存放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文件夹。 ├── PROCESSED_DATA：用于存储当前会话数据处理结果的文件夹集合，包含以下子目录： │ ├── BATHY：从任务日志中提取的测深原始数据输出目录。 │ ├── FRAMES：从DCIM文件夹视频中提取的带地理参考帧影像输出目录。 │ ├── 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。 软件说明 所有原始数据均通过我们的标准化工作流进行处理，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。祝您使用SeatizenDOI开展研究顺利！