Fire-Image-DenseNet (FIDN) for predicting wildfire burnt area using remote sensing data

TL;DR

The paper introduces FIDN, a deep learning model that accurately predicts wildfire burnt areas using remote sensing data, outperforming existing models in accuracy and speed, aiding firefighting strategies.

Contribution

Developed FIDN, a novel deep learning model that effectively predicts wildfire burnt areas across diverse landscapes, surpassing traditional physics-based models in accuracy and computational efficiency.

Findings

FIDN achieves 82% lower MSE than CA models.

FIDN's SSIM averages 97%, outperforming other models.

FIDN is about 1000 times faster than CA and MTT models.

Abstract

Predicting the extent of massive wildfires once ignited is essential to reduce the subsequent socioeconomic losses and environmental damage, but challenging because of the complexity of fire behaviour. Existing physics-based models are limited in predicting large or long-duration wildfire events. Here, we develop a deep-learning-based predictive model, Fire-Image-DenseNet (FIDN), that uses spatial features derived from both near real-time and reanalysis data on the environmental and meteorological drivers of wildfire. We trained and tested this model using more than 300 individual wildfires that occurred between 2012 and 2019 in the western US. In contrast to existing models, the performance of FIDN does not degrade with fire size or duration. Furthermore, it predicts final burnt area accurately even in very heterogeneous landscapes in terms of fuel density and flammability. The FIDN model showed higher accuracy, with a mean squared error (MSE) about 82% and 67% lower than those of the predictive models based on cellular automata (CA) and the minimum travel time (MTT) approaches, respectively. Its structural similarity index measure (SSIM) averages 97%, outperforming the CA and FlamMap MTT models by 6% and 2%, respectively. Additionally, FIDN is approximately three orders of magnitude faster than both CA and MTT models. The enhanced computational efficiency and accuracy advancements offer vital insights for strategic planning and resource allocation for firefighting operations.