Projecting multiclass global land-use and land-cover change using deep learning and spherical geographic automata model

Projecting multiclass global land-use and land-cover change using deep learning and spherical geographic automata model

Modelling land-use/landcover (LULC) change is vital for addressing global environmental and sustainability issues and evaluating various land system scenarios. However, existing geosimulation methodologies for global LULC change fail to account for spatial distortions caused by the Earth’s curvature...

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Bibliographic Details
Main Authors:	Bright Addae, Suzana Dragićević, Kirsten Zickfeld, Peter Hall
Format:	Article
Language:	English
Published:	Taylor & Francis Group 2025-01-01
Series:	Big Earth Data
Subjects:	Global land-use/land-cover change modelling deep learning spherical geographic automata geographic information systems shared socioeconomic pathways
Online Access:	https://www.tandfonline.com/doi/10.1080/20964471.2024.2386091
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