DESARROLLO DE UN ALGORITMO GLOBAL DE ÁREA QUEMADA PARA IMÁGENES DEL SENSOR ENVISAT-MERIS
Bibliographic record
Abstract
En este articulo se presentan los avances recientes en el desarrollo de un algoritmo para cartografiar area quemada a partir de imagenes MERIS. El trabajo se ha desarrollado en el ambito del programa de cambio climatico de la ESA. El analisis de resultados de la primera version del algoritmo condujo a implementar una serie de tests para mejorar la configuracion. Se probaron estos tests en 4 zonas: Australia, Canada, California y Peninsula Iberica, cubriendo un area total de 2.500.000 km2. Los resultados obtenidos para los distintos tests se compararon con los perimetros de area quemada generados por los gestores de incendios en cada zona, identificando mejoras sustanciales respecto a la primera version del algoritmo. Para el ano 2008, se obtuvo una fiabilidad global de 0,982 (frente al 0,975 de la version anterior). Esta nueva version del algoritmo se utilizara para procesar la serie completa de datos MERIS (2002-2012).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".