Gftt: an open-source tool for evaluating remotely sensed glacier velocity products
Bibliographic record
Abstract
Glacier velocity reflects the dynamics of ice flow, and its change over time serves a key role in predicting the future sea-level rise. Glacier feature tracking (also known as offset tracking or pixel tracking) is one of the most widely-used approaches for mapping glacier velocity using remote sensing data. However, running this workflow relies on multiple empirical parameter choices such as correlation kernel selection, image filter, and template size. As each target glacier area has different data availability, surface feature density, and ice flow width, there is no one-size-fits-all parameter set for glacier feature tracking. Finding an ideal parameter set for a given glacier requires quantitative and objective metrics to determine the quality of resulting velocity maps. The objective of our Glacier feature tracking test (gftt) project is both to devise a set of widely applicable metrics and to build a Python-based tool for calculating them. These metrics can be thus used for comparing the performance of different tracking parameters. We use Kaskawulsh glacier, Canada, as a test case to compare the velocity mapping results using Landsat 8 and Sentinel-2 images, various software packages (including Auto-RIFT, CARST, GIV, and vmap), and a range of input parameters. To begin with, we calculate random error over stable terrain, a metric that has been used for evaluating the uncertainty of the velocity products. We develop two other workflows for exploring new metrics and validating existing metrics, including the test with synthetic pixel offsets and the comparison with GNSS records. These existing and new metrics, calculated through the gftt software, will help determine optimal parameter sets for feature tracking of Kaskawulsh glacier and any other glacier around the world. This work is supported by the NSF Earth Cube Program under awards 1928406, 1928374.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".