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Australian sea-floor survey data, with images and expert annotations
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 org...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4623458/ https://www.ncbi.nlm.nih.gov/pubmed/26528396 http://dx.doi.org/10.1038/sdata.2015.57 |
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author | Bewley, Michael Friedman, Ariell Ferrari, Renata Hill, Nicole Hovey, Renae Barrett, Neville Pizarro, Oscar Figueira, Will Meyer, Lisa Babcock, Russ Bellchambers, Lynda Byrne, Maria Williams, Stefan B. |
author_facet | Bewley, Michael Friedman, Ariell Ferrari, Renata Hill, Nicole Hovey, Renae Barrett, Neville Pizarro, Oscar Figueira, Will Meyer, Lisa Babcock, Russ Bellchambers, Lynda Byrne, Maria Williams, Stefan B. |
author_sort | Bewley, Michael |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4623458 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-46234582015-11-02 Australian sea-floor survey data, with images and expert annotations Bewley, Michael Friedman, Ariell Ferrari, Renata Hill, Nicole Hovey, Renae Barrett, Neville Pizarro, Oscar Figueira, Will Meyer, Lisa Babcock, Russ Bellchambers, Lynda Byrne, Maria Williams, Stefan B. Sci Data Data Descriptor 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. Nature Publishing Group 2015-10-27 /pmc/articles/PMC4623458/ /pubmed/26528396 http://dx.doi.org/10.1038/sdata.2015.57 Text en Copyright © 2015, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0 This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0 Metadata associated with this Data Descriptor is available at http://www.nature.com/sdata/ and is released under the CC0 waiver to maximize reuse. |
spellingShingle | Data Descriptor Bewley, Michael Friedman, Ariell Ferrari, Renata Hill, Nicole Hovey, Renae Barrett, Neville Pizarro, Oscar Figueira, Will Meyer, Lisa Babcock, Russ Bellchambers, Lynda Byrne, Maria Williams, Stefan B. Australian sea-floor survey data, with images and expert annotations |
title | Australian sea-floor survey data, with images and expert annotations |
title_full | Australian sea-floor survey data, with images and expert annotations |
title_fullStr | Australian sea-floor survey data, with images and expert annotations |
title_full_unstemmed | Australian sea-floor survey data, with images and expert annotations |
title_short | Australian sea-floor survey data, with images and expert annotations |
title_sort | australian sea-floor survey data, with images and expert annotations |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4623458/ https://www.ncbi.nlm.nih.gov/pubmed/26528396 http://dx.doi.org/10.1038/sdata.2015.57 |
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