Projecting multiclass global land-use and land-cover change using deep learning and spherical geographic automata model-Reference-Cited by-同舟云学术

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

Published:2024-08-20 Issue: Volume: Page:1-28
ISSN:2096-4471
Container-title:Big Earth Data
language:en
Short-container-title:Big Earth Data

Author:

Addae Bright¹^ORCID,Dragićević Suzana¹^ORCID,Zickfeld Kirsten²^ORCID,Hall Peter³^ORCID

Affiliation:

1. Spatial Analysis and Modeling Laboratory, Department of Geography, Simon Fraser University, Burnaby, BC, Canada

2. Climate Research Laboratory, Department of Geography, Simon Fraser University, Burnaby, BC, Canada

3. Urban Studies Program, Simon Fraser University, Vancouver, BC, Canada

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/20964471.2024.2386091

Reference85 articles.

1. Enabling geosimulations for global scale: Spherical geographic automata

2. Modelling global urban land-use change process using spherical cellular automata

3. Reducing Emissions from Deforestation and Forest Degradation

4. A global wildfire dataset for the analysis of fire regimes and fire behaviour

5. The Law of Population Concentration

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