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.
Highlights
Background & SummaryLess than 0.05% of the global sea floor has been mapped with sonar swath mapping[1] at high resolution
The growing maturity of autonomous underwater vehicle (AUV) has permitted broader and more systematic visual surveys than traditional diver held cameras or towed video sleds
Image and sensor data was gathered by the AUV Sirius, as described in ref
Summary
Less than 0.05% of the global sea floor has been mapped with sonar swath mapping[1] at high resolution (tens of meters). The availability of a set of high quality expert labels with geographic and temporal diversity will permit researchers in these fields to investigate ways to reduce or eliminate the manual labeling effort, as well as gaining new scientific insights from working with a combined data set. Another significant hurdle to the integrated analysis of benthic imagery data is the Figure 1. Individual research groups have labeled images using a variety of custom labeling systems and standards suited to their particular geographic region and research interests, which limits the ability to perform scientific analysis, or train machine learning algorithms on large, varied data sets.
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