Near-Real-Time Wildfire Segmentation and Brightness Temperature Estimation from Geostationary Satellite Imagery Using Deep Learning

Abstract

Near-real-time wildfire monitoring is essential for situational awareness and emergency response. The Geostationary Operational Environmental Satellite (GOES) observes at 2 km every 5 min, whereas the Visible Infrared Imaging Radiometer Suite (VIIRS) observes at 375 m twice daily, leaving neither sensor able to track rapidly evolving fires in fine spatial detail. This study introduces a two-step deep learning approach that statistically infers VIIRS-like sub-pixel fire segmentation and brightness temperature (BT) from GOES imagery. The dataset comprises 257 U.S. wildfires within preselected regions of interest, pairing multi-spectral GOES imagery with VIIRS-derived references in four classes (Non-Fire, Small, Medium, and Large) defined by the number of fire pixels. Against an autoencoder-based baseline, the proposed method raises average precision from 0.105 to 0.368 and reduces the fire region BT root mean square error by 72.1% from 81.33 K to 22.69 K. Omission error rates fall from 9.41% to 1.96% for Medium fires and remain at zero for Large fires, whereas Small fires, 37% of the test set, remain at 39.91%. The false positive rate on Non-Fire images falls from 66.86% to 8.48%, near the operational GOES product’s 6.36% with lower omission (31.40% versus 40.35%). Inference completes within 5 min of observation for a monitored region.