A Lightweight Real-Time Disaster Assessment Semantic Segmentation Model for Autonomous Aerial Vehicles Remote Sensing

Liang Zhao, Xuebin Zhou, Ammar Hawbani, Na Lin, Lianbo Ma, Qiang He, Majjed Al-Qatf

Abstract: Semantic segmentation of high-resolution remote sensing imagery plays a critical role in applications such as disaster assessment. However, deploying large models on Autonomous Aerial Vehicles (AAVs) remains challenging due to inherent conflicts among accuracy, model size, and computational efficiency. To address these challenges, we propose FMC-ULite, a novel lightweight architecture designed to achieve a better balance between accuracy and efficiency for real-time processing. Our model incorporates four key innovations, including a Fast Fourier Transform (FFT)-based fusion module for enhanced edge feature extraction and noise suppression in the frequency domain, a simplified MobileNetV3-Large encoder that substantially reduces parameter count, a cross-layer feature fusion (CLFF) module to effectively integrate multi-scale semantic and detail information, and an attention-gated decoder with multi-scale dilated convolutions to prioritize critical disaster regions. Furthermore, an adaptive combined loss function is introduced to alleviate class imbalance. Experiments conducted on the RescueNet dataset show that our model achieves competitive accuracy compared to advanced lightweight methods under a comparable parameter budget, demonstrating its strong suitability for real-time disaster assessment using AAVs.

External IDs:doi:10.1109/jiot.2026.3670382