Landform mapping, elevation modelling, and thaw subsidence estimation for permafrost terrain using a consumer-grade remotely-piloted aircraft
Bibliographic record
Abstract
We assess performance of a small consumer-grade remotely-piloted aircraft (RPA) for landform mapping, elevation modelling, and thaw subsidence estimation in continuous permafrost terrain. We acquired RPA imagery near Rankin Inlet, Nunavut, to construct orthomosaics and digital elevation models (DEMs) that we use to interpret geomorphology and surficial geology. We estimate seasonal thaw subsidence using DEM differences. To quantify accuracy, RPA DEMs are compared with a satellite-based reference elevation. Subsidence estimates are compared with measurements from differential interferometric synthetic aperture radar (DInSAR). We find that RPA images are very effective for mapping periglacial landforms and surficial geology with the chosen flight specifications. The DEMs exhibit vertical mean absolute error of approximately 1 cm at ground control points. Away from control points, relative vertical accuracy is approximately 3 cm. Comparison to the reference elevation results in survey-wide vertical mean absolute errors of 33–66 cm with high variability and spatial autocorrelation of elevation discrepancy. There is local agreement between DEM differences, DInSAR, and on-the-ground measurements of seasonal subsidence. Results suggest that small RPA may be applicable for mapping thaw subsidence on the order of a few centimetres near control points. However, DEM differences are influenced by vegetation and are contaminated by spatially-variable artefacts, preventing reliable survey-wide RPA estimation of seasonal thaw subsidence.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".