Real-time forecast of high-resolution wildfire spread via Fast Cross-Scale Deep Learning

Abstract

The increasing frequency and severity of wildfires, particularly in the wildland-urban interface, underscores the urgent need for advanced real-time wildfire forecast models. This study develops a cross-scale deep-learning model for high-resolution wildfire emergency management that uses wildfires in Hong Kong Island, China as a demonstration. We simulate massive wildfire scenarios with a high spatial resolution of 5 m, based on historical fire records, and establish a numerical dataset of 240 fire cases (8640 samples of burnt area developing from a spot to vast landscape). Then, we introduce a cross-scale framework to achieve high-resolution wildfire spread forecast by avoiding the high-cost direct deep learning of high-resolution images. The framework forecasts the small-scale fire with 5-m resolution in the first 12 h and then smoothly transitions to 40-m resolution for forecasting the large-scale fire. The model is demonstrated to forecast the wildfire front and burning region crossing the spatial scale from 25 m2 to 20 km2 and achieve an overall accuracy of above 75% with a lead time ranging from 2h to 72 h. Finally, we develop a practical software, Intelligent Wildfire Forecast Tool (IWFTool), to integrate the cross-scale AI framework for supporting wildfire emergency response. The proposed smart framework enables the application of accurate, low-cost and fast-training AI tools for high-resolution wildfire forecasts and emergency responses. Graphical abstract: [Figure not available: see fulltext.]