Wildfire Spread Prediction in North America Using Satellite Imagery and Vision Transformer

The recent surge in wildfire incidents, exacerbated by climate change, demands effective prediction and monitoring solutions. This study introduces a novel deep learning model, the Attention Swin U-net with Focal Modulation (ASUFM), designed to predict wildfire spread in North America using large-scale remote sensing data. The ASUFM model integrates spatial attention and focal modulation into the Swin U-net architecture, significantly enhancing predictive performance on the Next Day Wildfire Spread (NDWS) benchmark. Additionally, we have developed an expanded dataset that encompasses wildfire data across North America from 2012 to 2023, providing a more extensive basis for model training and validation. Our approach demonstrates state-of-the-art performance in wildfire spread prediction, offering a promising tool for risk mitigation and resource allocation in wildfire management. Related code is in our public repository https://github.com/bronteee/fire-asufm