Spatially varying WIndow based maximum likelihood feature tracking (SWIFT) method for glacier surface velocity estimations
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Bibliographic record
Abstract
Glacier surface velocity is an important variable for glacier dynamics studies. Estimation of accurate surface velocity from remote sensing is a challenge, especially for glaciers with no in-situ observations. To overcome this challenge, a new method for glacier feature tracking named as Spatially varying WIndow based maximum likelihood Feature Tracking (SWIFT) has been proposed. This method utilizes both optical data (to automatically determine the window size [WS] using the concept of Object Based Image Analysis [OBIA]) and Synthetic Aperture Radar (SAR) data (to perform feature tracking). The proposed method uses a spatially varying WS unlike other existing softwares that cannot provide the flexibility of a spatially varying WS. The proposed method has been tested and validated at three different glaciers (South Glacier [SG], Canada; Chhota Shigri Glacier [CSG], India; and Tasman Glacier [TG], New Zealand) for which field measured data were available. The obtained results for all three glaciers showed consistent improvement in estimated velocity by SWIFT when compared with spatially fixed WS-based estimates from normalized cross correlation-based Correlation Image Analysis Software (CIAS). Considering the data availability, the proposed SWIFT method has been implemented using a variety of SAR and optical satellite data to understand its performance/effectiveness for glacier surface velocity estimation. When validated against field measurements, the results from SWIFT gave an RMSE of 12.8 m/years, 15.32 m/years and 67.1 m/years for SG, CSG and TG, respectively. Moreover, the RMSE of SWIFT estimates were observed to have an RMSE that was 19–36% lower than the best performing spatially fixed WS.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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