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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Nature Research
2022
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Online Access: | http://nora.nerc.ac.uk/id/eprint/530071/ https://nora.nerc.ac.uk/id/eprint/530071/1/s41597-022-01377-4%20%281%29.pdf https://www.nature.com/articles/s41597-022-01377-4 |
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ftnerc:oai:nora.nerc.ac.uk:530071 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 Fretwell, Peter 2022-05-27 text http://nora.nerc.ac.uk/id/eprint/530071/ https://nora.nerc.ac.uk/id/eprint/530071/1/s41597-022-01377-4%20%281%29.pdf https://www.nature.com/articles/s41597-022-01377-4 en eng Nature Research https://nora.nerc.ac.uk/id/eprint/530071/1/s41597-022-01377-4%20%281%29.pdf Cubaynes, Hannah orcid:0000-0002-9497-154X Fretwell, Peter orcid:0000-0002-1988-5844 . 2022 Whales from space dataset, an annotated satellite image dataset of whales for training machine learning models. Scientific Data, 9, 245. https://doi.org/10.1038/s41597-022-01377-4 <https://doi.org/10.1038/s41597-022-01377-4> cc_by_4 CC-BY Publication - Article PeerReviewed 2022 ftnerc https://doi.org/10.1038/s41597-022-01377-4 2023-02-04T19:51:57Z 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). Article in Journal/Newspaper Balaenoptera physalus Eubalaena glacialis Fin whale Humpback Whale Megaptera novaeangliae Southern Right Whale Natural Environment Research Council: NERC Open Research Archive New Zealand Argentina Scientific Data 9 1 |
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Open Polar |
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Natural Environment Research Council: NERC Open Research Archive |
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ftnerc |
language |
English |
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 |
Article in Journal/Newspaper |
author |
Cubaynes, Hannah Fretwell, Peter |
spellingShingle |
Cubaynes, Hannah Fretwell, Peter Whales from space dataset, an annotated satellite image dataset of whales for training machine learning models |
author_facet |
Cubaynes, Hannah Fretwell, Peter |
author_sort |
Cubaynes, Hannah |
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 Research |
publishDate |
2022 |
url |
http://nora.nerc.ac.uk/id/eprint/530071/ https://nora.nerc.ac.uk/id/eprint/530071/1/s41597-022-01377-4%20%281%29.pdf https://www.nature.com/articles/s41597-022-01377-4 |
geographic |
New Zealand Argentina |
geographic_facet |
New Zealand Argentina |
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_relation |
https://nora.nerc.ac.uk/id/eprint/530071/1/s41597-022-01377-4%20%281%29.pdf Cubaynes, Hannah orcid:0000-0002-9497-154X Fretwell, Peter orcid:0000-0002-1988-5844 . 2022 Whales from space dataset, an annotated satellite image dataset of whales for training machine learning models. Scientific Data, 9, 245. https://doi.org/10.1038/s41597-022-01377-4 <https://doi.org/10.1038/s41597-022-01377-4> |
op_rights |
cc_by_4 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1038/s41597-022-01377-4 |
container_title |
Scientific Data |
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9 |
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1 |
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1766366986687741952 |