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dc.contributor.authorLim, Nick Jin Seanen_NZ
dc.contributor.authorBifet, Alberten_NZ
dc.contributor.authorBull, Dainelen_NZ
dc.contributor.authorFrank, Eibeen_NZ
dc.contributor.authorJia, Yunzheen_NZ
dc.contributor.authorMontiel, Jacoben_NZ
dc.contributor.authorPfahringer, Bernharden_NZ
dc.date.accessioned2022-11-01T00:06:33Z
dc.date.available2022-11-01T00:06:33Z
dc.date.issued2022en_NZ
dc.identifier.issn0303-6758en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/15309
dc.description.abstractProper management of the earth's natural resources is imperative to combat further degradation of the natural environment. However, the environmental datasets necessary for informed resource planning and conservation can be costly to collect and annotate. Consequently, there is a lack of publicly available datasets, particularly annotated image datasets relevant for environmental conservation, that can be used for the evaluation of machine learning algorithms to determine their applicability in real-world scenarios. To address this, the Time-evolving Data Science and Artificial Intelligence for Advanced Open Environmental Science (TAIAO) project in New Zealand aims to provide a collection of datasets and accompanying example notebooks for their analysis. This paper showcases three New Zealand-based annotated image datasets that form part of the collection. The first dataset contains annotated images of various predator species, mainly small invasive mammals, taken using low-light camera traps predominantly at night. The second provides aerial photography of the Waikato region in New Zealand, in which stands of Kahikatea (a native New Zealand tree) have been marked up using manual segmentation. The third is a dataset containing orthorectified high-resolution aerial photography, paired with satellite imagery taken by Sentinel-2. Additionally, the TAIAO web platform also contains a collated list of other datasets provided and licensed by our data partners that may be of interest to other researchers.
dc.format.mimetypeapplication/pdf
dc.language.isoenen_NZ
dc.publisherInforma UK Limiteden_NZ
dc.rightsThis is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the Royal Society of New Zealand on September 19, 2022, available at: http://www.tandfonline.com/10.1080/03036758.2022.2118321.
dc.subjectcomputer scienceen_NZ
dc.subjectresearch resourceen_NZ
dc.subjectenvironmental scienceen_NZ
dc.subjectimage dataseten_NZ
dc.subjectaerial photographyen_NZ
dc.subjectcamera trapsen_NZ
dc.subjectremote sensingen_NZ
dc.subjectmachine learningen_NZ
dc.titleShowcasing the TAIAO project: providing resources for machine learning from images of New Zealand's natural environmenten_NZ
dc.typeJournal Article
dc.identifier.doi10.1080/03036758.2022.2118321en_NZ
dc.relation.isPartOfJournal of the Royal Society of New Zealanden_NZ
pubs.begin-page1
pubs.elements-id298544
pubs.end-page13
pubs.publication-statusPublished onlineen_NZ
dc.identifier.eissn1175-8899en_NZ


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