1 New Algorithms for Detecting Forest Fires on a Global Scale 2 From MODIS Time Series Analysis 3 4 5 6 7
Bibliographic record
Abstract
Abstract. Mapping forest fires globally is an important task for supporting climate and carbon cycle studies. There are two primary approaches to fire mapping: field- and aerial-based surveys, which are costly and limited in their extent; and satellite remote sensing-based approaches, which are more cost-effective but pose several interesting methodological and algorithmic challenges. In this paper, we describe evaluate a new algorithm framework for mapping forest fires based on satellite observations from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. A systematic comparison and validation against ground truth sources with alternate approaches across diverse geographic regions demonstrates that our algorithmic paradigm is able to overcome many of the limitations in both data and methods employed by prior efforts. We quantitatively show that the new framework out-performs the well-known MODIS Burned Area (BA) framework in the states of California (US), Georgia (US), Yukon, (Canada), and Victoria (Australia). Results demonstrate that our new framework is highly robust to noise in one of its primary inputs, MODIS Active Fires (AF), which is known to have low precision. 32 33
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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 | 0.002 | 0.005 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".