Underwater images collected by an Underwater Vision Census in Toliara, Madagascar - 2021-12-15

This dataset was collected by an Underwater Vision Census in Toliara, Madagascar - 2021-12-15. 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. Survey information Camera: Not enough information in metadata to get camera information. Number of images: 174 Total size: 0.02 Gb Mission start: 2021:12:15 06:21:41 Mission end: 2021:12:15 06:49:37 Mission duration: 0h 27min 56sec Total distance: 401 m GPS information: Surveys were conducted during low spring tides on reef areas less than 20 meters deep. A GPS device, kept in a floating waterproof bag at the surface, recorded a position every 2 seconds while following the diver's path. One diver took a benthic photo every 5 meters using a compass for direction, while a second diver guided the GPS bag from the surface. Image positions were interpolated with GPS data through time synchronization, using the timestamps embedded in the image metadata. Details of the acquisition method can be found in this paper. 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. │ ├── CPCE_ANNOTATION : All cpc files annotations made with the CPCe software. │ ├── IA : destination folder for image recognition predictions. │ └── PHOTOGRAMMETRY : destination folder for reconstructed models in photogrammetry. Software All the raw data was processed using our worflow. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!本数据集由2021年12月15日在马达加斯加图利亚拉开展的水下视觉普查采集。 科学家或公众采集的水下、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，用于训练人工智能模型以实现图像目标识别。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、物种或生境识别预测，并辅助生成地图。 ### 调查信息 相机：元数据中未提供足够信息以获取相机相关参数。 图像数量：174张 总文件大小：0.02 Gb 任务开始时间：2021:12:15 06:21:41 任务结束时间：2021:12:15 06:49:37 任务持续时长：0小时27分56秒 总行进距离：401米 ### GPS信息 本次调查在春汛低潮期开展于水深不足20米的礁区。一台置于水面漂浮防水袋中的GPS设备，每2秒记录一次位置，同步追踪潜水员的行进路径。一名潜水员借助罗盘定向，每行进5米拍摄一张底栖生物照片，另一名潜水员则在水面操控GPS设备收纳袋。通过图像元数据内嵌的时间戳，结合GPS时间同步数据，可对图像拍摄位置进行插值补全。采集方法的详细细节可参阅本论文。 ### 通用文件夹结构 YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number ├── DCIM：用于存储采集到的视频与照片等多媒体文件的文件夹。 ├── GPS：用于存储各类定位相关文件的文件夹。若可对文件进行校正（例如基于RINEX数据的后处理运动学解算），则需区分设备数据与基准站数据；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，直接将文件置于GPS文件夹根目录下。 │ ├── BASE：来自RTK基站或其他静态定位仪器的文件。 │ └── 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：用于存储当前会话数据处理结果的各类子文件夹。 │ ├── CPCE_ANNOTATION：使用CPCe软件生成的所有CPC格式标注文件。 │ ├── IA：图像识别预测结果的目标存储文件夹。 │ └── PHOTOGRAMMETRY：摄影测量重建模型的目标存储文件夹。 ### 软件 所有原始数据均通过我们的工作流完成处理。你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！