Very high resolution aerial image orthomosaics, point clouds, and elevation datasets of select permafrost landscapes in Alaska and northwestern Canada
Bibliographic record
Abstract
Abstract. Permafrost landscapes in the Arctic are highly vulnerable to warming, with rapid changes underway. High-resolution remote sensing, especially aerial datasets, offers valuable insights into current permafrost characteristics and thaw dynamics. Here, we present a new dataset of very high resolution orthomosaics, point clouds, and digital surface models that we acquired over permafrost landscapes in northwestern Canada and northern and northwestern Alaska for the purpose of better understanding the impacts of climate change on permafrost landscapes. 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 a 16 MP resolution. We processed the images to four-band (blue–green–red–near-infrared) orthomosaics and digital surface models with spatial resolutions of 7 to 20 cm as well as 3D point clouds with point densities of up to 41 points m−2. The dataset collection features 102 subprojects from 35 target regions (1.4–161.1 km2 in size). Project sizes range from 4.8 to 336 GB. In total, 3.17 TB were published. The horizontal precision of the datasets is in the range of 1–2 px and vertical precision is better than 0.10 m. The datasets are not radiometrically calibrated. Overall, these very high resolution images and point clouds provide significant opportunities for mapping permafrost landforms and generating detailed training datasets for machine learning, can serve as a baseline for change detection for thermokarst and thermo-erosion processes, and help with upscaling of field measurements to lower-resolution satellite observations. The dataset is available on the PANGAEA repository at https://doi.org/10.1594/PANGAEA.961577 (Rettelbach et al., 2024).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".