Monitoring Arctic Permafrost – Examining the Contribution of Volunteered Geographic Information to Mapping Ice-Wedge Polygons
Bibliographic record
Abstract
Abstract. This study evaluates the potential of Volunteered Geographic Information (VGI) for mapping and monitoring ice-wedge polygons in Arctic permafrost regions through two case studies in Alaska and Canada. We developed and tested a web-based mapping application that enables volunteers to identify polygon centroids in high-resolution aerial imagery, with data collected from 105 contributors in the frame of organized mapping events. The volunteer-contributed data achieved completeness scores of 88.74 % and 70.81 % compared to expert mapping for the Cape Blossom (Alaska) and Blueberry Hills (Canada) study regions respectively, with median positional accuracies of 1.29 m and 1.38 m. Analysis shows that contributions from approximately five volunteers per polygon are sufficient to achieve reliable results. Using Voronoi diagrams derived from the crowd-sourced centroids, we successfully reconstructed ice-wedge polygon networks and extracted key geomorphological and hydrological parameters including polygon area, perimeter, and network topology. The results demonstrate that VGI can effectively support permafrost monitoring by enabling efficient mapping of ice-wedge polygons across large areas while maintaining high data quality standards.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".