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 17.49 GB of MP4 files, which were trimmed into 4360 frames (at 2997/1000 fps). The frames are georeferenced. 99.4% of these extracted images are useful and 0.6% 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: 99.01 %, Q2: 0.96 %, Q5: 0.03 % Bathymetry The data are collected using a single-beam echosounder S500. 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 0.121 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10月21日由自主水面航行器（Autonomous Surface Vehicle）在塞舌尔阿尔达布拉Arm01区域采集。 科学家或普通民众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。此类影像可经标注后共享，用于训练人工智能（AI）模型，进而实现影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、物种或生境预测，以及测绘制图。 本数据集隶属于更大规模的Seatizen Atlas数据集集合，后者收录了大量水下与航空影像。相关研究方法、工具集及科学目标已在专属数据论文中详细阐述。 ### 图像采集 本次采集任务生成了总容量17.49 GB的MP4视频文件，经裁切后得到4360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，提取的图像中99.4%为有效样本，剩余0.6%为无效样本。针对有效帧，我们已使用DinoVd'eau模型完成多标签预测。 ### GPS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 GPS固定站或可提供校正帧的静态定位设备的文件。 - 设备GPS：采用Emlid Reach M2设备。 - 数据质量分布：Q1占比99.01%，Q2占比0.96%，Q5占比0.03%。 ### 水深测量 本数据集使用单波束测深仪（single-beam echosounder）S500采集数据。 我们仅保留带有Q1级GPS校正的测深值，同时保留航点数据。 仅保留水深估算值介于0.2 m至40.0 m之间的原始数据。 数据处理初始阶段以WGS84椭球体为参考基准。处理完成后，数据被投影至统一格网以生成栅格（raster）文件与形状文件（shapefiles），格网单元尺寸为0.121 m。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 命名格式：YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number ├── DCIM：用于存储采集到的视频与照片的文件夹。 ├── 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，直接将文件放置于GPS文件夹根目录。 │ ├── BASE：来自RTK站或静态定位设备的基准站文件。 │ └── 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：包含存储当前任务数据处理结果所需的所有子文件夹。 │ ├── BATHY：用于存储从任务日志中提取的水深原始数据的输出文件夹。 │ ├── FRAMES：用于存储从DCIM视频中提取的地理配准帧的输出文件夹。 │ ├── IA：用于存储图像识别预测结果的目标文件夹。 │ └── PHOTOGRAMMETRY：用于存储摄影测量重建模型的目标文件夹。 └── 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、自动驾驶仪的日志文件、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完成处理，所有预测结果均由我们的推理管线生成。你可在本代码仓库中找到下载该数据集所需的全部脚本。愿你借助Seatizen DOI顺利使用本数据集！