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SAGRNet: A novel object-based graph convolutional neural network for diverse vegetation cover classification in remotely-sensed imagery

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dc.contributor.authorGui, Baoling
dc.contributor.authorSam, Lydia
dc.contributor.authorBhardwaj, Anshuman
dc.contributor.authorGómez, Diego Soto
dc.contributor.authorPeñaloza, Félix González
dc.contributor.authorBuchroithner, Manfred F.
dc.contributor.authorGreen, David R.
dc.contributor.institutionUniversity of Aberdeen.Geosciencesen
dc.contributor.institutionUniversity of Aberdeen.Cryosphere and Climate Change Research Groupen
dc.contributor.institutionUniversity of Aberdeen.Planetary Sciencesen
dc.contributor.institutionUniversity of Aberdeen.Geography & Environmenten
dc.date.accessioned2025-07-24T14:39:00Z
dc.date.available2025-07-24T14:39:00Z
dc.date.issued2025-09
dc.descriptionOpen Access via the Elsevier agreement We would like to thank the UK Centre for Ecology and Hydrology (UKCEH) for providing the high-resolution land cover maps of the United Kingdom, which served as an essential reference in our regional experiments. We also express our appreciation to ESRI for releasing the Global Land Cover dataset through the Living Atlas platform, which enabled the global-scale validation of our model. These openly accessible and high-quality datasets significantly supported the development and evaluation of our work.en
dc.description.statusPeer revieweden
dc.format.extent26
dc.format.extent38576019
dc.identifier304837500
dc.identifier55c85bcf-0402-4493-9f3a-48ef5e94e1f3
dc.identifier105007819714
dc.identifier.citationGui, B, Sam, L, Bhardwaj, A, Gómez, D S, Peñaloza, F G, Buchroithner, M F & Green, D R 2025, 'SAGRNet: A novel object-based graph convolutional neural network for diverse vegetation cover classification in remotely-sensed imagery', ISPRS Journal of Photogrammetry and Remote Sensing, vol. 227, pp. 99-124. https://doi.org/10.1016/j.isprsjprs.2025.06.004en
dc.identifier.doi10.1016/j.isprsjprs.2025.06.004
dc.identifier.issn0924-2716
dc.identifier.otherRIS: urn:8265C02772301133809E4CD08F86C02F
dc.identifier.otherORCID: /0000-0002-2502-6384/work/186084923
dc.identifier.otherORCID: /0000-0002-0518-9979/work/186084944
dc.identifier.urihttps://hdl.handle.net/2164/25755
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0924271625002308en
dc.identifier.vol227en
dc.language.isoeng
dc.relation.ispartofISPRS Journal of Photogrammetry and Remote Sensingen
dc.subjectSDG 13 - Climate Actionen
dc.subjectObject-based classificationen
dc.subjectGraph convolutionalen
dc.subjectVegetation mappingen
dc.subjectDeep learningen
dc.subjectRemote sensingen
dc.subjectQE Geologyen
dc.subject.lccQEen
dc.titleSAGRNet: A novel object-based graph convolutional neural network for diverse vegetation cover classification in remotely-sensed imageryen
dc.typeJournal articleen

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