Structural Similarity-Guided Siamese U-Net Model for Detecting Changes in Snow Water Equivalent
Bibliographic record
Abstract
Snow water equivalent (SWE), the amount of water generated when a snowpack melts, has been used to study the impacts of climate change on the cryosphere processes and snow cover dynamics during the winter season. In most analyses, high-temporal-resolution SWE and SD data are aggregated into monthly and yearly averages to detect and characterize changes. Aggregating snow measurements, however, can magnify the modifiable aerial unit problem, resulting in differing snow trends at different temporal resolutions. Time series analysis of gridded SWE data holds the potential to unravel the impacts of climate change and global warming on daily, weekly, and monthly changes in snow during the winter season. Consequently, this research presents a high-temporal-resolution analysis of changes in the SWE across the cold regions of Canada. A Siamese UNet (Si-UNet) was developed by modifying the model’s last layer to incorporate the structural similarity (SSIM) index. The similarity values from the SSIM index are passed to a contrastive loss function, where the optimization process maximizes SSIM index values for pairs of similar SWE images and minimizes the values for pairs of dissimilar SWE images. A comparison of different model architectures, loss functions, and similarity metrics revealed that the SSIM index and the contrastive loss improved the Si-UNet’s accuracy by 16%. Using our Si-UNet, we found that interannual SWE declined steadily from 1979 to 2018, with March being the month in which the most significant changes occurred (R2 = 0.1, p-value < 0.05). We conclude with a discussion on the implications of the findings from our study of snow dynamics and climate variables using gridded SWE data, computer vision metrics, and fully convolutional deep neural networks.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".