Mapping land cover in the low Arctic using multiple sources of earth observation data
Bibliographic record
Abstract
Land cover maps in high latitude regions are important for permafrost modeling and mapping and other climate change related studies. National and global land cover products cannot meet the requirements for regional permafrost mapping due to their broad land cover types. This study produced a land cover map in a low Arctic region using multi-source earth observation data, including different modes of RADARSAT-2 (RS2), Sentinel 2 (S2), and the high resolution ArcticDEM. We assessed the performances of two machine learning classification algorithms (Random Forest (RF) and Support Vector Machine (SVM)) with different combinations of input data, including evaluating the effects of incidence angle from different RS2 modes on classification results. This study concludes that the combination of two imaging modes of RS2, S2 composites and the high resolution ArcticDEM achieves a more accurate land cover map (the overall accuracy is 93.6%) than other combinations with the overall accuracies ranging from 38% to 87%; the performance of RF is better than that of SVM, the overall accuracy difference ranges from 21% to 1%; and the shallow SAR incidence angles outperform steep ones in classification results of different combinations for both classifiers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 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".