Arctic circumpolar permafrost region building footprints from <1 meter resolution Maxar satellite imagery and OpenStreetMap, (2018-2023)
Bibliographic record
Abstract
This product is a geospatial vector layer containing two-dimensional footprints of buildings (i.e., spatial extent covered by an individual building on the ground) across the Arctic circumpolar permafrost region. The data is based on building footprints from OpenStreetMap (OSM) contributions within Arctic regions, then built upon by filling in missing areas with building footprints detected from less than 1 m (meter) spatial resolution, summertime, cloud-free Maxar satellite imagery of Arctic circumpolar permafrost communities. The building detection workflow is named HABITAT (High-resolution Arctic Built Infrastructure and Terrain Analysis Tool) and the combined dataset is thus named HABITAT-OSM. Building footprints are provided in the North Pole Lambert Azimuthal Equal Area projection. The provided data spans 32 first-level administrative regions (the largest subnational administrative unit within a country) spanning the Arctic: Alaska (US), Yukon, Northwest Territories, Nunavut, Newfoundland and Labrador, Northern Quebec (Canada), all Greenland regions, all Iceland regions, Nordland, Troms, Finnmark, Svalbard (Norway), Norbotten (Sweden), Lappi (Finland), Komi, Arkhangelsk, Nenets, Khanty-Mansi, Yamalo-Nenets, Krasnoyarsk Krai, Sakha Republic, Kamchatka, Magadan, and Chukotka (Russia).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.012 |
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".