Reconstructing Historical Landsat Time Series Using a Transformer-Based Deep Learning Approach: A Case Study in the Canadian Prairies Region
Bibliographic record
Abstract
The Landsat program is crucial for monitoring environmental changes, benefiting from its long record of moderate-spatial-resolution imagery, well-established calibration, and commitment to providing open-access data. However, the sparse and irregular observation intervals in Landsat time series pose challenges for applications requiring temporally consistent data. This study evaluates the performance of two deep learning models, CFC-mmRNN and a Transformer-based network, for reconstructing Landsat time series under varying data availability conditions. Accuracy is analyzed across spectral bands, seasons, and data densities to assess their effectiveness in handling irregular temporal gaps. The results indicate that the Transformer model outperforms CfC-mmRNN. While both models achieve similar accuracy in high-density cases, CfC-mmRNN’s performance declines sharper as data density decreases. In contrast, the Transformer model maintains the temporal structure of the reconstructed time series even at very low densities, with about two observations per year. These findings suggest that CfC-mmRNN remains effective for time series applications when sufficient observations are available, and it is directly applicable to forecasting tasks. The Transformer model, however, offers a more robust solution for reconstructing sparse Landsat time series, particularly in data-scarce conditions. This study underscores the importance of selecting an appropriate deep learning method to enhance Landsat time series reconstruction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".