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A fully-annotated imagery dataset of sublittoral benthic species in Svalbard, Arctic

Underwater imagery is widely used for a variety of applications in marine biology and environmental sciences, such as classification and mapping of seabed habitats, marine environment monitoring and impact assessment, biogeographic reconstructions in the context of climate change, etc. This approach...

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Autores principales: Šiaulys, Andrius, Vaičiukynas, Evaldas, Medelytė, Saulė, Olenin, Sergej, Šaškov, Aleksej, Buškus, Kazimieras, Verikas, Antanas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7873376/
https://www.ncbi.nlm.nih.gov/pubmed/33604435
http://dx.doi.org/10.1016/j.dib.2021.106823
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author Šiaulys, Andrius
Vaičiukynas, Evaldas
Medelytė, Saulė
Olenin, Sergej
Šaškov, Aleksej
Buškus, Kazimieras
Verikas, Antanas
author_facet Šiaulys, Andrius
Vaičiukynas, Evaldas
Medelytė, Saulė
Olenin, Sergej
Šaškov, Aleksej
Buškus, Kazimieras
Verikas, Antanas
author_sort Šiaulys, Andrius
collection PubMed
description Underwater imagery is widely used for a variety of applications in marine biology and environmental sciences, such as classification and mapping of seabed habitats, marine environment monitoring and impact assessment, biogeographic reconstructions in the context of climate change, etc. This approach is relatively simple and cost-effective, allowing the rapid collection of large amounts of data. However, due to the laborious and time-consuming manual analysis procedure, only a small part of the information stored in the archives of underwater images is retrieved. Emerging novel deep learning methods open up the opportunity for more effective, accurate and rapid analysis of seabed images than ever before. We present annotated images of the bottom macrofauna obtained from underwater video recorded in Spitsbergen island's European Arctic waters, Svalbard Archipelago. Our videos were filmed in both the photic and aphotic zones of polar waters, often influenced by melting glaciers. We used artificial lighting and shot close to the seabed (<1 m) to preserve natural colours and avoid the distorting effect of muddy water. The underwater video footage was captured using a remotely operated vehicle (ROV) and a drop-down camera. The footage was converted to 2D mosaic images of the seabed. 2D mosaics were manually annotated by several experts using the Labelbox tool and co-annotations were refined using the SurveyJS platform. A set of carefully annotated underwater images associated with the original videos can be used by marine biologists as a biological atlas, as well as practitioners in the fields of machine vision, pattern recognition, and deep learning as training materials for the development of various tools for automatic analysis of underwater imagery.
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spelling pubmed-78733762021-02-17 A fully-annotated imagery dataset of sublittoral benthic species in Svalbard, Arctic Šiaulys, Andrius Vaičiukynas, Evaldas Medelytė, Saulė Olenin, Sergej Šaškov, Aleksej Buškus, Kazimieras Verikas, Antanas Data Brief Data Article Underwater imagery is widely used for a variety of applications in marine biology and environmental sciences, such as classification and mapping of seabed habitats, marine environment monitoring and impact assessment, biogeographic reconstructions in the context of climate change, etc. This approach is relatively simple and cost-effective, allowing the rapid collection of large amounts of data. However, due to the laborious and time-consuming manual analysis procedure, only a small part of the information stored in the archives of underwater images is retrieved. Emerging novel deep learning methods open up the opportunity for more effective, accurate and rapid analysis of seabed images than ever before. We present annotated images of the bottom macrofauna obtained from underwater video recorded in Spitsbergen island's European Arctic waters, Svalbard Archipelago. Our videos were filmed in both the photic and aphotic zones of polar waters, often influenced by melting glaciers. We used artificial lighting and shot close to the seabed (<1 m) to preserve natural colours and avoid the distorting effect of muddy water. The underwater video footage was captured using a remotely operated vehicle (ROV) and a drop-down camera. The footage was converted to 2D mosaic images of the seabed. 2D mosaics were manually annotated by several experts using the Labelbox tool and co-annotations were refined using the SurveyJS platform. A set of carefully annotated underwater images associated with the original videos can be used by marine biologists as a biological atlas, as well as practitioners in the fields of machine vision, pattern recognition, and deep learning as training materials for the development of various tools for automatic analysis of underwater imagery. Elsevier 2021-01-30 /pmc/articles/PMC7873376/ /pubmed/33604435 http://dx.doi.org/10.1016/j.dib.2021.106823 Text en © 2021 The Authors. Published by Elsevier Inc. http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Šiaulys, Andrius
Vaičiukynas, Evaldas
Medelytė, Saulė
Olenin, Sergej
Šaškov, Aleksej
Buškus, Kazimieras
Verikas, Antanas
A fully-annotated imagery dataset of sublittoral benthic species in Svalbard, Arctic
title A fully-annotated imagery dataset of sublittoral benthic species in Svalbard, Arctic
title_full A fully-annotated imagery dataset of sublittoral benthic species in Svalbard, Arctic
title_fullStr A fully-annotated imagery dataset of sublittoral benthic species in Svalbard, Arctic
title_full_unstemmed A fully-annotated imagery dataset of sublittoral benthic species in Svalbard, Arctic
title_short A fully-annotated imagery dataset of sublittoral benthic species in Svalbard, Arctic
title_sort fully-annotated imagery dataset of sublittoral benthic species in svalbard, arctic
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7873376/
https://www.ncbi.nlm.nih.gov/pubmed/33604435
http://dx.doi.org/10.1016/j.dib.2021.106823
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