A landscape metrics-based sample weighting approach for forecasting land cover change with deep learning models
Bibliographic record
Abstract
Unaddressed imbalance of multitemporal land cover (LC) data reduces deep learning (DL) model usefulness to forecast changes. To manage geospatial data imbalance, there is a lack of specialized cost-sensitive learning strategies available. Sample weights are typically derived from training instance frequencies, which disregard spatial pattern complexities. Therefore, this study proposes a geospatial sample weighting approach underpinned by class-level landscape metrics (LSMs) to assign importance to categories based on relative indicators of spatial form. A case study demonstrates the application and effects of the LSM-based sample weighting approach for projecting LC changes of a region in British Columbia, Canada. Four spatiotemporal DL models are provided weighted training samples including multitemporal LC data and explanatory factors. Sample weights calculated from indicators of patch density, shape irregularity, and shape heterogeneity improved figure of merit and related measures over baseline configurations. This study contributes to LC change data imbalance management strategies for DL models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".