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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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-01-01
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| Series: | Big Earth Data |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/20964471.2024.2386091 |
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| author | Bright Addae Suzana Dragićević Kirsten Zickfeld Peter Hall |
| author_facet | Bright Addae Suzana Dragićević Kirsten Zickfeld Peter Hall |
| author_sort | Bright Addae |
| collection | DOAJ |
| description | 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 and do not consider multiple LULC change processes. Thus, this research study proposes an enhanced spherical geosimulation modelling approach that integrates deep learning (DL) to simulate change of multiple classes of LULC process under the shared socioeconomic pathways (SSP) at the global level. Based on the simulation results, the frontiers of urbanization, cropland expansion, and deforestation are indicated to be in developing countries particularly in Asia and Africa. The simulation outputs also reveal 42.5%–63.2% of new urban development would occur on croplands. The proposed modelling approach can serve as a valuable tool for spatial decision-making and environmental policy formulation at the global level. |
| format | Article |
| id | doaj-art-d92dd97c0a4e4e48835c89d242cce57d |
| institution | OA Journals |
| issn | 2096-4471 2574-5417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Big Earth Data |
| spelling | doaj-art-d92dd97c0a4e4e48835c89d242cce57d2025-08-20T02:16:29ZengTaylor & Francis GroupBig Earth Data2096-44712574-54172025-01-019112810.1080/20964471.2024.2386091Projecting multiclass global land-use and land-cover change using deep learning and spherical geographic automata modelBright Addae0Suzana Dragićević1Kirsten Zickfeld2Peter Hall3Spatial Analysis and Modeling Laboratory, Department of Geography, Simon Fraser University, Burnaby, BC, CanadaSpatial Analysis and Modeling Laboratory, Department of Geography, Simon Fraser University, Burnaby, BC, CanadaClimate Research Laboratory, Department of Geography, Simon Fraser University, Burnaby, BC, CanadaUrban Studies Program, Simon Fraser University, Vancouver, BC, CanadaModelling 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 and do not consider multiple LULC change processes. Thus, this research study proposes an enhanced spherical geosimulation modelling approach that integrates deep learning (DL) to simulate change of multiple classes of LULC process under the shared socioeconomic pathways (SSP) at the global level. Based on the simulation results, the frontiers of urbanization, cropland expansion, and deforestation are indicated to be in developing countries particularly in Asia and Africa. The simulation outputs also reveal 42.5%–63.2% of new urban development would occur on croplands. The proposed modelling approach can serve as a valuable tool for spatial decision-making and environmental policy formulation at the global level.https://www.tandfonline.com/doi/10.1080/20964471.2024.2386091Global land-use/land-cover change modellingdeep learningspherical geographic automatageographic information systemsshared socioeconomic pathways |
| spellingShingle | Bright Addae Suzana Dragićević Kirsten Zickfeld Peter Hall Projecting multiclass global land-use and land-cover change using deep learning and spherical geographic automata model Big Earth Data Global land-use/land-cover change modelling deep learning spherical geographic automata geographic information systems shared socioeconomic pathways |
| title | Projecting multiclass global land-use and land-cover change using deep learning and spherical geographic automata model |
| title_full | Projecting multiclass global land-use and land-cover change using deep learning and spherical geographic automata model |
| title_fullStr | Projecting multiclass global land-use and land-cover change using deep learning and spherical geographic automata model |
| title_full_unstemmed | Projecting multiclass global land-use and land-cover change using deep learning and spherical geographic automata model |
| title_short | Projecting multiclass global land-use and land-cover change using deep learning and spherical geographic automata model |
| title_sort | projecting multiclass global land use and land cover change using deep learning and spherical geographic automata model |
| topic | Global land-use/land-cover change modelling deep learning spherical geographic automata geographic information systems shared socioeconomic pathways |
| url | https://www.tandfonline.com/doi/10.1080/20964471.2024.2386091 |
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