Wildfire spread forecasting with Deep Learning

TL;DR

This paper introduces a deep learning framework that uses multi-day spatio-temporal data to accurately forecast wildfire spread, significantly improving prediction metrics over baseline models.

Contribution

The study presents a novel deep learning approach leveraging multi-day observational data for wildfire spread forecasting, with an ablation study on temporal data inclusion.

Findings

Multi-day data improves predictive accuracy.

Best model with 4 days before to 5 days after ignition outperforms baseline.

F1 score and IoU increased by nearly 5%.

Abstract

Accurate prediction of wildfire spread is crucial for effective risk management, emergency response, and strategic resource allocation. In this study, we present a deep learning (DL)-based framework for forecasting the final extent of burned areas, using data available at the time of ignition. We leverage a spatio-temporal dataset that covers the Mediterranean region from 2006 to 2022, incorporating remote sensing data, meteorological observations, vegetation maps, land cover classifications, anthropogenic factors, topography data, and thermal anomalies. To evaluate the influence of temporal context, we conduct an ablation study examining how the inclusion of pre- and post-ignition data affects model performance, benchmarking the temporal-aware DL models against a baseline trained exclusively on ignition-day inputs. Our results indicate that multi-day observational data substantially improve predictive accuracy. Particularly, the best-performing model, incorporating a temporal window of four days before to five days after ignition, improves both the F1 score and the Intersection over Union by almost 5% in comparison to the baseline on the test dataset. We publicly release our dataset and models to enhance research into data-driven approaches for wildfire modeling and response.