Whales from space dataset, an annotated satellite image dataset of whales for training machine learning models

Monitoring whales in remote areas is important for their conservation; however, using traditional survey platforms (boat and plane) in such regions is logistically difficult. The use of very high-resolution satellite imagery to survey whales, particularly in remote locations, is gaining interest and...

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Bibliographic Details
Published in:Scientific Data
Main Authors: Cubaynes, Hannah C., Fretwell, Peter T.
Format: Text
Language:English
Published: Nature Publishing Group UK 2022
Subjects:
Data Descriptor
Argentina
New Zealand
Balaenoptera physalus
Eubalaena glacialis
Fin whale
Humpback Whale
Megaptera novaeangliae
Southern Right Whale
Online Access:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9142526/
http://www.ncbi.nlm.nih.gov/pubmed/35624202
https://doi.org/10.1038/s41597-022-01377-4
id ftpubmed:oai:pubmedcentral.nih.gov:9142526
record_format openpolar
spelling ftpubmed:oai:pubmedcentral.nih.gov:9142526 2023-05-15T15:36:37+02:00 Whales from space dataset, an annotated satellite image dataset of whales for training machine learning models Cubaynes, Hannah C. Fretwell, Peter T. 2022-05-27 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9142526/ http://www.ncbi.nlm.nih.gov/pubmed/35624202 https://doi.org/10.1038/s41597-022-01377-4 en eng Nature Publishing Group UK http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9142526/ http://www.ncbi.nlm.nih.gov/pubmed/35624202 http://dx.doi.org/10.1038/s41597-022-01377-4 © 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/) . CC-BY Sci Data Data Descriptor Text 2022 ftpubmed https://doi.org/10.1038/s41597-022-01377-4 2022-06-05T00:47:38Z Monitoring whales in remote areas is important for their conservation; however, using traditional survey platforms (boat and plane) in such regions is logistically difficult. The use of very high-resolution satellite imagery to survey whales, particularly in remote locations, is gaining interest and momentum. However, the development of this emerging technology relies on accurate automated systems to detect whales, which are currently lacking. Such detection systems require access to an open source library containing examples of whales annotated in satellite images to train and test automatic detection systems. Here we present a dataset of 633 annotated whale objects, created by surveying 6,300 km2 of satellite imagery captured by various very high-resolution satellites (i.e. WorldView-3, WorldView-2, GeoEye-1 and Quickbird-2) in various regions across the globe (e.g. Argentina, New Zealand, South Africa, United States, Mexico). The dataset covers four different species: southern right whale (Eubalaena glacialis), humpback whale (Megaptera novaeangliae), fin whale (Balaenoptera physalus), and grey whale (Eschrichtius robustus). Text Balaenoptera physalus Eubalaena glacialis Fin whale Humpback Whale Megaptera novaeangliae Southern Right Whale PubMed Central (PMC) Argentina New Zealand Scientific Data 9 1
institution Open Polar
collection PubMed Central (PMC)
op_collection_id ftpubmed
language English
topic Data Descriptor
spellingShingle Data Descriptor
Cubaynes, Hannah C.
Fretwell, Peter T.
Whales from space dataset, an annotated satellite image dataset of whales for training machine learning models
topic_facet Data Descriptor
description Monitoring whales in remote areas is important for their conservation; however, using traditional survey platforms (boat and plane) in such regions is logistically difficult. The use of very high-resolution satellite imagery to survey whales, particularly in remote locations, is gaining interest and momentum. However, the development of this emerging technology relies on accurate automated systems to detect whales, which are currently lacking. Such detection systems require access to an open source library containing examples of whales annotated in satellite images to train and test automatic detection systems. Here we present a dataset of 633 annotated whale objects, created by surveying 6,300 km2 of satellite imagery captured by various very high-resolution satellites (i.e. WorldView-3, WorldView-2, GeoEye-1 and Quickbird-2) in various regions across the globe (e.g. Argentina, New Zealand, South Africa, United States, Mexico). The dataset covers four different species: southern right whale (Eubalaena glacialis), humpback whale (Megaptera novaeangliae), fin whale (Balaenoptera physalus), and grey whale (Eschrichtius robustus).
format Text
author Cubaynes, Hannah C.
Fretwell, Peter T.
author_facet Cubaynes, Hannah C.
Fretwell, Peter T.
author_sort Cubaynes, Hannah C.
title Whales from space dataset, an annotated satellite image dataset of whales for training machine learning models
title_short Whales from space dataset, an annotated satellite image dataset of whales for training machine learning models
title_full Whales from space dataset, an annotated satellite image dataset of whales for training machine learning models
title_fullStr Whales from space dataset, an annotated satellite image dataset of whales for training machine learning models
title_full_unstemmed Whales from space dataset, an annotated satellite image dataset of whales for training machine learning models
title_sort whales from space dataset, an annotated satellite image dataset of whales for training machine learning models
publisher Nature Publishing Group UK
publishDate 2022
url http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9142526/
http://www.ncbi.nlm.nih.gov/pubmed/35624202
https://doi.org/10.1038/s41597-022-01377-4
geographic Argentina
New Zealand
geographic_facet Argentina
New Zealand
genre Balaenoptera physalus
Eubalaena glacialis
Fin whale
Humpback Whale
Megaptera novaeangliae
Southern Right Whale
genre_facet Balaenoptera physalus
Eubalaena glacialis
Fin whale
Humpback Whale
Megaptera novaeangliae
Southern Right Whale
op_source Sci Data
op_relation http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9142526/
http://www.ncbi.nlm.nih.gov/pubmed/35624202
http://dx.doi.org/10.1038/s41597-022-01377-4
op_rights © 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/) .
op_rightsnorm CC-BY
op_doi https://doi.org/10.1038/s41597-022-01377-4
container_title Scientific Data
container_volume 9
container_issue 1
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