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

This dataset was collected by an Autonomous Surface Vehicle in Aldabra-Dubois, 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 15.27 GB of MP4 files, which were trimmed into 4696 frames (at 2997/1000 fps). The frames are georeferenced. 78.66% of these extracted images are useful and 21.34% 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: 91.85 %, Q2: 7.54 %, Q5: 0.61 % 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 2.0 m and 20.0 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 0.728 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-迪布瓦地区采集。 科研人员与公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。上述图像可经标注后共享，用于训练人工智能模型以实现图像目标识别。本项目提供一套软硬件工具集，可用于海洋数据采集、物种/生境预测及地图制作。 本数据集是包含海量水下与航拍图像的大型数据集集合Seatizen Atlas的组成部分。相关研究方法、工具及科学目标已在专属数据论文中详述。 图像采集 本次采集任务包含总容量15.27 GB的MP4视频文件，经裁切后得到4696帧图像（帧率为2997/1000 fps）。所有图像帧均已完成地理配准。经Jacques模型预测，其中78.66%的提取图像为有效样本，剩余21.34%为无效样本。针对有效图像帧，已通过DinoVd'eau模型完成多标签预测。 GPS信息 本数据集采用PPK（Post-Processed Kinematic，后处理运动学）工作流进行处理，实现了厘米级的GPS定位精度。 基准站数据：来自RTK（Real-Time Kinematic，实时动态差分）固定GPS站或可提供校正帧的静态定位设备的文件。 设备GPS：Emlid Reach M2 数据质量分级：Q1占比91.85%，Q2占比7.54%，Q5占比0.61% 测深数据 本次测深数据通过单波束测深仪S500采集。仅保留带有Q1级GPS校正的测深值。仅保留航点处的测深点数据。仅保留水深估算值介于2.0米至20.0米之间的原始测深数据。数据首先以WGS84椭球为基准进行坐标系匹配。处理完成后，数据被投影至统一格网，生成栅格数据与形状文件（shapefiles）。格网单元尺寸为0.728米。栅格与形状文件通过线性插值法生成，三维重建算法采用ballpivot算法。 通用文件夹结构 命名规则：YYYYMMDD_COUNTRYCODE-可选地点_设备_会话-编号 ├── DCIM：用于存储采集到的视频与图像文件的目录。 ├── GPS：用于存储所有与定位相关的文件。若文件可通过后处理技术（如基于RINEX（Receiver Independent Exchange Format，接收机独立交换格式）数据的PPK校正）进行精度修正，则需将基准站数据与设备数据分目录存放；若仅存在设备定位数据且无法通过后处理修正（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目录根路径下。 │ ├── BASE：存储来自RTK基准站或静态定位设备的基准数据。 │ └── DEVICE：存储来自采集设备的定位数据。 ├── METADATA：存储本次采集任务的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集合。 │ ├── BATHY：存储从任务日志中提取的原始测深数据的输出目录。 │ ├── FRAMES：存储从DCIM目录视频中提取的地理配准图像帧的输出目录。 │ ├── 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：用于存储其他来源文件的目录，如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本项目自研工作流完成处理，所有预测结果均由本项目推理管线生成。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顺利！