Exploring remote sensing data with high temporal resolutions for wildfire spread prediction

Abstract

The severity of wildfires has been steadily increasing in the United States over the past few decades, burning up many millions of acres and costing billions of dollars in suppression efforts each year. However, in the same few decades there have been great strides made to advance our technological capabilities. Machine learning is one such technology that has seen spectacular improvements in many areas such as computer vision and natural language processing, and is now being used extensively to model spatiotemporal phenomena such as wildfires via deep learning. Leveraging deep learning to model how wildfires spread can help facilitate evacuation efforts and assist wildland firefighters by highlighting key areas where containment and suppression efforts should be focused. Many recent works have examined the feasibility of using deep learning models to predict when and where wildfires will spread to, which has been enabled in part due to the wealth of geospatial information that is now publicly available and easily accessible on platforms such as Google Earth Engine. In this work, the First Week Wildfire Spread dataset is introduced, which seeks to address some of the limitations with previously released datasets by having an increased focus on geospatial data with high temporal resolutions. The new dataset contains weather, fuel, topography, and fire location data for the first 7 days of 56 megafires that occurred in the Contiguous United States from 2020 to 2024. Fire location data is collected by the Advanced Baseline Imager aboard the GOES-16 satellite, which provides updates every 5 minutes. Baseline experiments are performed using U-Net and ConvLSTM models to demonstrate some of the various ways that the First Week Wildfire Spread dataset can be used and to highlight its versatility.