Gui, BaolingSam, LydiaBhardwaj, AnshumanGómez, Diego SotoPeñaloza, Félix GonzálezBuchroithner, Manfred F.Green, David R.2025-07-242025-07-242025-09Gui, 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.2025.06.0040924-2716RIS: urn:8265C02772301133809E4CD08F86C02FORCID: /0000-0002-2502-6384/work/186084923ORCID: /0000-0002-0518-9979/work/186084944https://hdl.handle.net/2164/25755Open 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.2638576019engSDG 13 - Climate ActionObject-based classificationGraph convolutionalVegetation mappingDeep learningRemote sensingQE GeologyQEJournal article10.1016/j.isprsjprs.2025.06.004https://www.sciencedirect.com/science/article/pii/S0924271625002308227