Burnt Area Monitoring In Near-Real Time - Combining High Spatial And Temporal Resolution
Bibliographic record
Abstract
Devastating fire events in Europe (e.g. Greece 2023, Spain 2022) but also on a global scale (e.g. Chile 2024, Canada 2023) show the importance to mitigate wildfire spreading as early as possible. This implies the availability of timely, accurate and robust information. The Center for Satellite-based Crisis Information (ZKI) Wildfire Monitoring System, operated at the German Aerospace Center (DLR), is a research platform providing satellite-derived burnt area information for Europe and North Africa. The information is provided in near-real time whenever new satellite overpasses are available. In order to provide the highest possible update frequency, the system supports a multitude of input sensors. While the system is primarily based on mid-resolution data, latest developments have been focussing on enhancing the results with higher-resolution data as soon as these data become available. This includes the DLR sensors DESIS and EnMap, the first two hyperspectral sources utilized by the system. However, since the sensors feature 235 and 222 spectral bands, respectively, efforts have been undertaken to define which bands yield the highest benefit for burnt area analysis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".