The paper shows that deriving a structured belief from the prediction operator's needs and using it in non-myopic scheduling yields up to 28% better predictive loss than activity-paced baselines on a physics-calibrated synthetic wildfire environment.
Meso- geos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.ET 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Belief-Aware Scheduling for Predictive Wildfire Hazard Mapping under Sparse-Window Telemetry
The paper shows that deriving a structured belief from the prediction operator's needs and using it in non-myopic scheduling yields up to 28% better predictive loss than activity-paced baselines on a physics-calibrated synthetic wildfire environment.