Burned Area Mapping of Boreal Forests Using Automatically Generated Samples
Bibliographic record
Abstract
Accurately mapping wildfire extent and dynamics in boreal forests is essential for climate and ecological research. Currently, only two medium-resolution burned area datasets, generated based on a large number of visual interpretation samples, are available for research on the entire Boreal Forest: the Global Forest Loss (Fire_GFL) and the Global Annual Burned Area Map (GABAM). Fire_GFL has an 8% omission in North America and globally, while GABAM suffers from severe "stripe" and "box" effects, making it unsuitable for use. Therefore, a new burned area dataset covering the boreal region is needed for more precise wildfire mapping. This study develops a large-scale fully automated wildfire extraction algorithm named BAMS for the boreal forests, which can automatically generate training samples. Its effectiveness and robustness are tested using the 2023 Canadian wildfires as a case study. Compared to the TIIC produced by Natural Resources Canada and the publicly released Fire_GFL, BAMS achieved the highest OA of 93%, compared to 91% and 86%, respectively. At the regional scale, BAMS shows a strong correlation and consistency with TIIC, while the forest burned area estimated by Fire_GFL is systematically lower than that of BAMS and TIIC. These preliminary results suggest that the proposed method can accurately and automatically delineate wildfire areas in boreal forests.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".