Underwater images collected by an Autonomous Surface Vehicle in Aldabra-Arm01, Seychelles - 2022-10-21

This dataset was collected by an Autonomous Surface Vehicle in Aldabra-Arm01, Seychelles - 2022-10-21. 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. Image acquisition This session has 23.67 GB of MP4 files, which were trimmed into 9012 frames (at 2997/1000 fps). The frames are georeferenced. 99.78% of these extracted images are useful and 0.22% are useless, according to predictions made by Jacques model. Multilabel predictions have been made on useful frames using DinoVd'eau model. 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: 87.97 %, Q2: 11.05 %, Q5: 0.98 % 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 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 0.644 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!本数据集由自主水面航行器（Autonomous Surface Vehicle）于2022年10月21日在塞舌尔阿尔达环礁Arm01海域采集。 科研人员或公众采集的水下与航空图像，可广泛应用于科学研究、资源管理与生态保护领域。此类图像经标注后可用于训练人工智能模型，进而实现图像内目标的自动识别。我们提供一套包含硬件与软件的工具集，可用于海洋数据采集、物种或栖息地识别以及地图绘制。 本数据集隶属于涵盖海量水下与航空图像的Seatizen Altas大型数据集集合。相关研究方法、工具集与科学目标已在专属数据论文中详细阐述。 ### 图像采集 本次采集任务生成23.67 GB的MP4视频文件，经裁切后得到9012帧图像（帧率为2997/1000 fps）。所有帧均已完成地理配准。根据雅克模型（Jacques model）的预测结果，其中99.78%的提取图像为有效样本，剩余0.22%为无效样本。针对有效帧，我们已通过DinoVd'eau模型完成多标签预测。 ### GPS定位信息 本数据采用后处理运动学（PPK, Post-Processed Kinematic）工作流进行处理，以实现厘米级的GPS定位精度。基准站数据：来自RTK固定GPS基站或可提供校正帧的静态定位设备。设备GPS：Emlid Reach M2。数据质量分级：Q1占比87.97%，Q2占比11.05%，Q5占比0.98%。 ### 水深测量 本数据通过单波束测深仪ETC 400采集。我们仅保留带有Q1级GPS校正的数据点以及航点数据，同时保留水深估计值介于0.2米至40.0米之间的原始数据。数据最初以WGS84椭球面为参考基准，经处理后被投影至统一格网，以生成栅格数据与形状文件（shapefiles）。格网单元尺寸为0.644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）。 ### 通用文件夹结构 采用YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number命名格式。 ├── DCIM：用于存储采集到的视频与照片文件的文件夹。 ├── GPS：用于存储所有与定位相关的文件。若可对文件进行校正（例如基于RINEX数据的后处理运动学校正），则需区分基准站数据与设备数据；若仅存在设备定位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，直接将文件放置于GPS文件夹根目录。 │ ├── BASE：来自RTK基站或静态定位设备的文件。 │ └── DEVICE：来自采集设备的文件。 ├── METADATA：存储本次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文件夹集合。 │ ├── BATHY：存储从任务日志中提取的水深原始数据的输出文件夹。 │ ├── FRAMES：存储从DCIM视频中提取的地理配准帧的输出文件夹。 │ ├── 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）。 ### 软件工具 所有原始数据均通过我们的工作流进行处理，所有预测结果均由我们的推理流水线生成。你可在本代码仓库中找到下载该数据集所需的全部脚本。使用SeatizenDOI畅享你的数据之旅！