Whales from space dataset, an annotated satellite image dataset of whales for training machine learning models
Abstract: 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 in...
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ftunivcam:oai:www.repository.cam.ac.uk:1810/338205 2023-07-30T04:02:31+02:00 Whales from space dataset, an annotated satellite image dataset of whales for training machine learning models Cubaynes, Hannah C. Fretwell, Peter T. 2022-06-17T16:01:01Z text/xml application/pdf https://doi.org/10.17863/CAM.85616 https://www.repository.cam.ac.uk/handle/1810/338205 en eng Nature Publishing Group UK Scientific Data doi:10.17863/CAM.85616 https://www.repository.cam.ac.uk/handle/1810/338205 Data Descriptor /631/158/672 /631/114/1564 data-descriptor Article 2022 ftunivcam https://doi.org/10.17863/CAM.85616 2023-07-10T21:44:33Z Abstract: 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 australis), humpback whale (Megaptera novaeangliae), fin whale (Balaenoptera physalus), and grey whale (Eschrichtius robustus). Article in Journal/Newspaper Balaenoptera physalus Fin whale Humpback Whale Megaptera novaeangliae Southern Right Whale Apollo - University of Cambridge Repository Argentina New Zealand |
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Apollo - University of Cambridge Repository |
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English |
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Data Descriptor /631/158/672 /631/114/1564 data-descriptor |
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Data Descriptor /631/158/672 /631/114/1564 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 /631/158/672 /631/114/1564 data-descriptor |
description |
Abstract: 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 australis), humpback whale (Megaptera novaeangliae), fin whale (Balaenoptera physalus), and grey whale (Eschrichtius robustus). |
format |
Article in Journal/Newspaper |
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 |
https://doi.org/10.17863/CAM.85616 https://www.repository.cam.ac.uk/handle/1810/338205 |
geographic |
Argentina New Zealand |
geographic_facet |
Argentina New Zealand |
genre |
Balaenoptera physalus Fin whale Humpback Whale Megaptera novaeangliae Southern Right Whale |
genre_facet |
Balaenoptera physalus Fin whale Humpback Whale Megaptera novaeangliae Southern Right Whale |
op_relation |
doi:10.17863/CAM.85616 https://www.repository.cam.ac.uk/handle/1810/338205 |
op_doi |
https://doi.org/10.17863/CAM.85616 |
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1772813334313172992 |