Mitigating Imbalance of Land Cover Change Data for Deep Learning Models with Temporal and Spatiotemporal Sample Weighting Schemes
Bibliographic record
Abstract
An open problem impeding the use of deep learning (DL) models for forecasting land cover (LC) changes is their bias toward persistent cells. By providing sample weights for model training, LC changes can be allocated greater influence in adjustments to model internal parameters. The main goal of this research study was to implement and evaluate temporal and spatiotemporal sample weighting schemes that manage the influence of persistent and formerly changed areas. The proposed sample weighting schemes allocate higher weights to more recently changed areas based on the inverse temporal and spatiotemporal distance from previous changes occurring at a location or within the location’s neighborhood. Four spatiotemporal DL models (CNN-LSTM, CNN-GRU, CNN-TCN, and ConvLSTM) were used to compare the sample weighting schemes to forecast the LC changes of the Columbia-Shuswap Regional District in British Columbia, Canada, using data obtained from the MODIS annual LC dataset and other auxiliary spatial variables. The results indicate that the presented weighting schemes facilitated improvement over no sample weighting and the common inverse frequency weighting scheme for multi-year LC change forecasts, lowering errors due to quantity while reducing overall allocation error severity. This research study contributes to strategies for addressing the characteristic imbalances of multitemporal LC change datasets for DL modeling endeavors.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.004 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".