澳大利亚海床调查数据，包括图像和专家注释。

Australian sea-floor survey data, with images and expert annotations.

机构信息

Australian Centre for Field Robotics, The University of Sydney , NSW 2006, Australia.

Australian Centre for Field Robotics, The University of Sydney , NSW 2006, Australia ; Coastal and Marine Ecosystem Group, School of Biological Sciences, The University of Sydney , NSW 2006, Australia.

出版信息

Sci Data. 2015 Oct 27;2:150057. doi: 10.1038/sdata.2015.57. eCollection 2015.

DOI:10.1038/sdata.2015.57

PMID:26528396

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4623458/

Abstract

This Australian benthic data set (BENTHOZ-2015) consists of an expert-annotated set of georeferenced benthic images and associated sensor data, captured by an autonomous underwater vehicle (AUV) around Australia. This type of data is of interest to marine scientists studying benthic habitats and organisms. AUVs collect georeferenced images over an area with consistent illumination and altitude, and make it possible to generate broad scale, photo-realistic 3D maps. Marine scientists then typically spend several minutes on each of thousands of images, labeling substratum type and biota at a subset of points. Labels from four Australian research groups were combined using the CATAMI classification scheme, a hierarchical classification scheme based on taxonomy and morphology for scoring marine imagery. This data set consists of 407,968 expert labeled points from around the Australian coast, with associated images, geolocation and other sensor data. The robotic surveys that collected this data form part of Australia's Integrated Marine Observing System (IMOS) ongoing benthic monitoring program. There is reuse potential in marine science, robotics, and computer vision research.

摘要

这个澳大利亚海底数据集（BENTHOZ-2015）由一组经过专家注释的地理参考海底图像和相关传感器数据组成，这些数据是由水下自主机器人（AUV）在澳大利亚周围海域采集的。这种类型的数据对于研究海底栖息地和生物的海洋科学家很有兴趣。AUV 可以在光照和高度一致的区域采集地理参考图像，并能够生成大规模、逼真的 3D 地图。然后，海洋科学家通常会在数千张图像中的每一张上花费几分钟的时间，在一小部分点上标记海底类型和生物。来自四个澳大利亚研究小组的标签使用 CATAMI 分类方案进行了组合，该分类方案是一种基于分类学和形态学的海洋图像评分分层分类方案。该数据集包含来自澳大利亚海岸的 407968 个经过专家标记的点，以及相关的图像、地理位置和其他传感器数据。收集这些数据的机器人调查是澳大利亚综合海洋观测系统（IMOS）正在进行的海底监测计划的一部分。在海洋科学、机器人技术和计算机视觉研究中具有重用潜力。