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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ftunivcam:oai:www.repository.cam.ac.uk:1810/338557 2024-02-04T09:59:06+01:00 Whales from space dataset, an annotated satellite image dataset of whales for training machine learning models. Cubaynes, Hannah C Fretwell, Peter T 2022-06-29T19:48:07Z application/pdf https://www.repository.cam.ac.uk/handle/1810/338557 https://doi.org/10.17863/CAM.85970 eng eng Springer Science and Business Media LLC http://dx.doi.org/10.1038/s41597-022-01377-4 Sci Data https://www.repository.cam.ac.uk/handle/1810/338557 doi:10.17863/CAM.85970 Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/ essn: 2052-4463 nlmid: 101640192 Animals Cetacea Fin Whale Humpback Whale Machine Learning Satellite Imagery United States Article 2022 ftunivcam https://doi.org/10.17863/CAM.85970 2024-01-11T23:30:10Z 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 Apollo - University of Cambridge Repository New Zealand Argentina |
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Open Polar |
collection |
Apollo - University of Cambridge Repository |
op_collection_id |
ftunivcam |
language |
English |
topic |
Animals Cetacea Fin Whale Humpback Whale Machine Learning Satellite Imagery United States |
spellingShingle |
Animals Cetacea Fin Whale Humpback Whale Machine Learning Satellite Imagery United States Cubaynes, Hannah C Fretwell, Peter T Whales from space dataset, an annotated satellite image dataset of whales for training machine learning models. |
topic_facet |
Animals Cetacea Fin Whale Humpback Whale Machine Learning Satellite Imagery United States |
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 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 |
Springer Science and Business Media LLC |
publishDate |
2022 |
url |
https://www.repository.cam.ac.uk/handle/1810/338557 https://doi.org/10.17863/CAM.85970 |
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_source |
essn: 2052-4463 nlmid: 101640192 |
op_relation |
https://www.repository.cam.ac.uk/handle/1810/338557 doi:10.17863/CAM.85970 |
op_rights |
Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.17863/CAM.85970 |
_version_ |
1789963754330390528 |