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dc.contributor.authorCubaynes, Hannah C.
dc.contributor.authorFretwell, Peter T.
dc.date.accessioned2022-06-17T16:01:02Z
dc.date.available2022-06-17T16:01:02Z
dc.date.issued2022-05-27
dc.date.submitted2021-04-21
dc.identifier.others41597-022-01377-4
dc.identifier.other1377
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/338205
dc.description.abstractAbstract: 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).
dc.languageen
dc.publisherNature Publishing Group UK
dc.subjectData Descriptor
dc.subject/631/158/672
dc.subject/631/114/1564
dc.subjectdata-descriptor
dc.titleWhales from space dataset, an annotated satellite image dataset of whales for training machine learning models
dc.typeArticle
dc.date.updated2022-06-17T16:01:01Z
prism.issueIdentifier1
prism.publicationNameScientific Data
prism.volume9
dc.identifier.doi10.17863/CAM.85616
dcterms.dateAccepted2022-05-06
rioxxterms.versionofrecord10.1038/s41597-022-01377-4
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidCubaynes, Hannah C. [0000-0002-9497-154X]
dc.identifier.eissn2052-4463
pubs.funder-project-idRCUK | NERC | British Antarctic Survey (BAS) (Innovation Voucher, NE/T012439/1, Innovation Vocuher, NE/T012439/1)


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