Projecting multiclass global land-use and land-cover change using deep learning and spherical geographic automata model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it