Super-high-resolution aerial imagery datasets of permafrost landscapes in Alaska and northwestern Canada
Bibliographic record
Abstract
Abstract. Permafrost landscapes across the Arctic are very susceptible to a warming climate and are currently experiencing rapid change. High-resolution remote sensing datasets present a valuable source of information to better analyze and quantify current permafrost landscape characteristics and impacts of climate change on the environment. In particular, aerial datasets can provide further understanding of permafrost landscapes in transition due to local and widespread thaw. We here present a new dataset of super-high-resolution digital orthophotos, photogrammetric point clouds, and digital surface models that we acquired over permafrost landscapes in northwestern Canada, northern, and western Alaska. The imagery was collected with the Modular Aerial Camera System (MACS) during aerial campaigns conducted by the Alfred Wegener Institute in the summers of 2018, 2019, and 2021. The MACS was specifically developed by the German Aerospace Center (DLR) for operation under challenging light conditions in polar environments. It features cameras in the optical and the near-infrared wavelengths with up to 16 megapixels. We processed the images to four-band (blue – green – red – near-infrared) orthomosaics, digital surface models with spatial resolutions of 7 to 20 cm, and 3D point clouds with point densities up to 44 pts/m3. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations. All three regional dataset collections, along with supporting data, are available via PANGAEA; the DOIs are listed in the Code and Data Availability Section.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".