Super-high-resolution Earth observation datasets of North American permafrost landscapes
Bibliographic record
Abstract
While temperatures are increasing on the global scale, the Arctic regions are especially vulnerable to this changing climate and landscapes underlain by permafrost experience increased thaw and degradation. The enhanced warming of organic-rich frozen ground can have severe consequences on infrastructure and ecosystems and is projected to become a highly relevant driver of greenhouse gas fluxes into the atmosphere. Degrading permafrost landscapes occur extensively in vast areas of the North American Arctic, directly affecting communities and ecosystems. To identify and quantify these widespread degradation phenomena over vast areas, we require highest-resolution Earth observation dataset that we collect during aerial imaging campaigns. \nWe here report on observations and first results from three airborne campaigns in 2018, 2019 and 2021. We performed large-scale monitoring of permafrost-affected areas in northern Canada and Alaska, focusing on sites that experienced disturbances in the past or recently. This included sites with vulnerable settlements, coastal erosion, thaw slumping, lake expansion and drainage, ice-wedge degradation and thaw subsidence, fire scars, pingos, methane seeps, and sites affected by beaver activities. \nAll surveys were flown with the Alfred Wegener Institute's Polar-5 and -6 scientific airplanes at 500-1500 m altitude above terrain. The onboard sensor, the Modular Aerial Camera System (MACS), a very-high-resolution multispectral camera developed by the German Aerospace Center, operated in the visible (RGB) and near-infrared (NIR) domain. \nFrom the comprehensive collection of multiple TB of gathered data, super-high-resolution (up to 7 cm/px) RGB+NIR image mosaics and stereophotogrammetric digital surface models were derived. \nBy presenting the data and first analyses, we would like to invite the community to discuss best use for maximized benefit of the data, in order to substantially contribute to our understanding of permafrost thaw dynamics.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".