Spatial Uncertainty Quantification in Wildfire Forecasting for Climate-Resilient Emergency Planning
This work addresses the need for risk-aware decision-making in wildfire management for communities adapting to climate change, though it is incremental as it builds on existing machine learning approaches.
The paper tackled the problem of lacking uncertainty quantification in wildfire spread forecasting by analyzing spatial uncertainty using multimodal Earth observation data, revealing that predictive uncertainty forms consistent 20-60 meter buffer zones around predicted firelines.
Climate change is intensifying wildfire risks globally, making reliable forecasting critical for adaptation strategies. While machine learning shows promise for wildfire prediction from Earth observation data, current approaches lack uncertainty quantification essential for risk-aware decision making. We present the first systematic analysis of spatial uncertainty in wildfire spread forecasting using multimodal Earth observation inputs. We demonstrate that predictive uncertainty exhibits coherent spatial structure concentrated near fire perimeters. Our novel distance metric reveals high-uncertainty regions form consistent 20-60 meter buffer zones around predicted firelines - directly applicable for emergency planning. Feature attribution identifies vegetation health and fire activity as primary uncertainty drivers. This work enables more robust wildfire management systems supporting communities adapting to increasing fire risk under climate change.