The frequency and severity of wildfires have been increasing due to extreme heat and drought conditions driven by climate change. Rapid and accurate assessment of burned areas immediately after wildfire events is essential for prioritizing restoration efforts and establishing effective disaster response strategies. This study proposes a post-processing framework that combines SegFormer with a superpixel-based non-iterative clustering technique for precise burned area estimation from high-resolution UAV imagery. SNIC-based superpixel aggregation was applied to the pixel-wise predictions from SegFormer to reduce salt-and-pepper noise and enhance spatial consistency along boundaries. Experimental results demonstrate that the proposed method improved precision from 84.22 % to 86.83 % and IoU from 88.17 % to 92.90 % compared to SegFormer alone, confirming overall improvements in the stability of damage masks and reliability of area estimation. Therefore, the proposed framework demonstrates the potential for rapid and reliable burned-area estimation in small- and medium-scale wildfire-affected areas and can serve as a practical analytical tool to support disaster response and recovery decision-making.