Burned Area

Burned area mapping is crucial for assessing wildfire impacts and informing post-fire management strategies. Current research focuses on automating this process using advanced remote sensing data (e.g., Landsat, Sentinel-2, SPOT) and artificial intelligence, particularly deep learning models like U-Net, UPerNet, and SegFormer, often incorporating multitask learning or anomaly detection techniques for improved accuracy and robustness. These efforts aim to provide timely and accurate burned area delineations, improving environmental monitoring, resource allocation, and risk assessment following wildfires. The development of scalable and unsupervised methods is particularly important for rapid response in emergency situations.