Underwater images collected by an Autonomous Surface Vehicle in Anakao, Madagascar - 2023-05-01

This dataset was collected by an Autonomous Surface Vehicle in Anakao, Madagascar - 2023-05-01. 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 13.58 GB of MP4 files, which were trimmed into 2767 frames (at 2997/1000 fps). The frames are georeferenced. 80.88% of these extracted images are useful and 19.12% 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: 0.0 %, Q2: 0.0 %, Q5: 100.0 % Bathymetry The data are collected using a single-beam echosounder ETC 400. We keep the points that are the waypoints. We keep the raw data where depth was estimated between 0.2 m and 50.0 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 0.335 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2023年5月1日由自主水面航行器（Autonomous Surface Vehicle）在马达加斯加阿纳考地区采集。 科学家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。此类图像可经标注后共享，用于训练人工智能（AI）模型以实现图像内目标的识别预测。本团队提供一套软硬件工具集，可用于海洋数据采集、物种/生境预测及地图生成。 本数据集是收录海量水下与航拍图像的大型数据集Seatizen Altas的组成部分。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 本次采集任务包含13.58GB的MP4视频文件，经剪辑后提取得到2767帧图像（帧率为2997/1000 fps）。所有提取帧均已完成地理配准。经Jacques模型预测，其中80.88%的提取图像为有效样本，19.12%为无效样本。针对有效样本帧，已通过DinoVd'eau模型完成多标签预测。 GPS定位信息 本数据集采用后处理运动学差分（PPK）处理流程，实现厘米级GPS定位精度。 基准站数据：源自GPS固定基准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0.0%，Q2占比0.0%，Q5占比100.0% 水深测量 本次测深数据采用单波束测深仪（single-beam echosounder）ETC 400采集。仅保留航点对应的测点数据。仅保留水深估算值介于0.2米至50.0米之间的原始数据。数据首先以WGS84椭球（WGS84 ellipsoid）为基准进行坐标配准。处理完成后，数据被投影至统一格网中，生成栅格文件（raster）与形状文件（shapefiles）。格网单元尺寸为0.335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 YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number ├── DCIM：用于存储采集所得视频与照片的文件夹。 ├── GPS：用于存储所有定位相关文件的文件夹。若文件可通过后处理技术（如基于RINEX数据的后处理运动学差分）进行校正，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（如GPX文件），则无需区分基准站与设备数据，文件直接存放于GPS文件夹根目录。 │ ├── BASE：来自RTK基准站或静态定位设备的文件。 │ └── DEVICE：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文件夹。 ├── PROCESSED_DATA：用于存储本次采集任务数据处理结果的所有子文件夹。 │ ├── BATHY：从任务日志中提取的测深原始数据输出文件夹。 │ ├── FRAMES：从DCIM文件夹视频中提取的地理配准帧输出文件夹。 │ ├── IA：图像识别预测结果存储文件夹。 │ └── PHOTOGRAMMETRY：摄影测量重建模型存储文件夹。 └── SENSORS：用于存储其他来源文件的文件夹（如测深仪采集的测深数据、自动驾驶仪日志、任务规划文件等）。 软件说明 所有原始数据均通过本团队开发的处理流程完成校正与处理，所有预测结果均由本团队的推理流水线生成。本仓库中包含下载该数据集所需的全部脚本文件。祝您使用SeatizenDOI获取的数据集顺利！