Lightweight Feature-Enhanced U-Net for Landslide Change Detection in Remote Sensing Imagery

Effective landslide change detection from remote sensing images is essential for disaster prevention and risk management. This paper introduces a lightweight neural network architecture that integrates a Feature Enhancement Block (FEB) with the SimAM attention module to improve change detection accuracy in complex terrains. Additionally, the use of Focal Tversky Loss helps optimize the model's focus on genuine changes. The proposed lightweight network architecture significantly accelerates processing on GPGPUs, enabling real-time detection capabilities. Experimental results demonstrate that our model achieves an 88% reduction in parameters while improving Intersection over Union (IoU) by 8.5% and Recall by 8.37% compared to the baseline, showcasing its efficiency and accuracy.