Arctic circumpolar permafrost region building footprints from <1 meter resolution Maxar satellite imagery and OpenStreetMap Version 2 (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-meter spatial resolution, summertime, cloud-free Maxar satellite imagery of Arctic circumpolar permafrost communities. The dataset was produced using the HABITAT (High-resolution Arctic Built Infrastructure and Terrain Analysis Tool) deep learning framework, which consists of a convolutional neural network (CNN) segmentation model and a GraphCNN classification model, as well as post-processing and quality control (vectorization, smoothing, false positive + small footprint removal). Building heights and number of stories are estimated based on the ArcticDEM circumpolar digital surface model. 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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.007 |
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; both teacher heads agree on what is shown here.
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".