A comprehensive land cover classification approach for the development of wildfire fuels layers
Bibliographic record
Abstract
Land use and land cover (LULC) information is an important component for wildfire modeling as an understanding of the underlying vegetation conditions is needed to propagate fires accurately across the region. While government entities around the globe often create their own maps of available fuels, political boundaries within a country or across international borders can create inaccuracies and mismatched labeling that changes the speed, direction, and characteristics of the modeled fire. Government-generated fuels layers can also utilize different classification schemes with the United States relying on the 13 category Anderson Fire Behavior Fuel Model or the 46 category Scott Burgan Fire Behavior Fuel Model and Canada using a 19 category Fire Behavior Prediction System. To address these differences and provide a higher resolution understanding of conditions on the ground, a methodology based on the ISODATA Unsupervised Classification method was developed and applied to 30-meter Landsat 8 imagery. The methodology was utilized to create a fuels layer for Australia in 2016 and the western provinces of Canada in 2017 and will be used in future updates to wildfire models at AIR Worldwide.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.001 |
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".