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Long-term High Resolution Image Dataset of Antarctic Coastal Benthic Fauna
Antarctica is a remote place, the continent is covered by ice and its surrounding coastal areas are frozen for the majority of the year. Due to its peculiarity the observation of the underwater organisms is particularly difficult, complicated by logistic factors. We present a long-term dataset consi...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9719491/ https://www.ncbi.nlm.nih.gov/pubmed/36463241 http://dx.doi.org/10.1038/s41597-022-01865-7 |
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author | Marini, Simone Bonofiglio, Federico Corgnati, Lorenzo Paolo Bordone, Andrea Schiaparelli, Stefano Peirano, Andrea |
author_facet | Marini, Simone Bonofiglio, Federico Corgnati, Lorenzo Paolo Bordone, Andrea Schiaparelli, Stefano Peirano, Andrea |
author_sort | Marini, Simone |
collection | PubMed |
description | Antarctica is a remote place, the continent is covered by ice and its surrounding coastal areas are frozen for the majority of the year. Due to its peculiarity the observation of the underwater organisms is particularly difficult, complicated by logistic factors. We present a long-term dataset consisting of 755 images acquired by using a non-invasive, autonomous imaging device and encompassing both the Antarctic daylight and dark periods, including the corresponding transition phases. All images have the same field of view showing the benthic fauna and part of the water column above, including fishes present in the monitored period. All the images are manually annotated after a visual inspection performed by expert biologists. The extended monitoring period and the annotated images make the dataset a valuable benchmark suitable for studying the dynamics of the long-term Antarctic underwater fauna as well as for developing and testing algorithms for automated image analysis focused on the recognition and classification of the Antarctic organisms and the automated analysis of their long-term dynamics. |
format | Online Article Text |
id | pubmed-9719491 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-97194912022-12-05 Long-term High Resolution Image Dataset of Antarctic Coastal Benthic Fauna Marini, Simone Bonofiglio, Federico Corgnati, Lorenzo Paolo Bordone, Andrea Schiaparelli, Stefano Peirano, Andrea Sci Data Data Descriptor Antarctica is a remote place, the continent is covered by ice and its surrounding coastal areas are frozen for the majority of the year. Due to its peculiarity the observation of the underwater organisms is particularly difficult, complicated by logistic factors. We present a long-term dataset consisting of 755 images acquired by using a non-invasive, autonomous imaging device and encompassing both the Antarctic daylight and dark periods, including the corresponding transition phases. All images have the same field of view showing the benthic fauna and part of the water column above, including fishes present in the monitored period. All the images are manually annotated after a visual inspection performed by expert biologists. The extended monitoring period and the annotated images make the dataset a valuable benchmark suitable for studying the dynamics of the long-term Antarctic underwater fauna as well as for developing and testing algorithms for automated image analysis focused on the recognition and classification of the Antarctic organisms and the automated analysis of their long-term dynamics. Nature Publishing Group UK 2022-12-03 /pmc/articles/PMC9719491/ /pubmed/36463241 http://dx.doi.org/10.1038/s41597-022-01865-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Marini, Simone Bonofiglio, Federico Corgnati, Lorenzo Paolo Bordone, Andrea Schiaparelli, Stefano Peirano, Andrea Long-term High Resolution Image Dataset of Antarctic Coastal Benthic Fauna |
title | Long-term High Resolution Image Dataset of Antarctic Coastal Benthic Fauna |
title_full | Long-term High Resolution Image Dataset of Antarctic Coastal Benthic Fauna |
title_fullStr | Long-term High Resolution Image Dataset of Antarctic Coastal Benthic Fauna |
title_full_unstemmed | Long-term High Resolution Image Dataset of Antarctic Coastal Benthic Fauna |
title_short | Long-term High Resolution Image Dataset of Antarctic Coastal Benthic Fauna |
title_sort | long-term high resolution image dataset of antarctic coastal benthic fauna |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9719491/ https://www.ncbi.nlm.nih.gov/pubmed/36463241 http://dx.doi.org/10.1038/s41597-022-01865-7 |
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