Enhanced Landslide Detection from Remote Sensing Imagery Using an Attention-Optimized UNet-CBAM

Abstract. Landslide deformation monitoring is crucial for disaster prevention and protecting infrastructure, ecosystems, and lives in vulnerable regions. Traditional methods, though useful, often lack the precision required for complex terrains, limiting their effectiveness in landslide-prone areas. This study presents the UNet-Convolutional Block Attention Module (CBAM) framework, which combines the UNet architecture with CBAM to enhance landslide detection and segmentation in remote sensing imagery. The integration of CBAM improves the model's ability to focus on spatially significant features, leading to more accurate and efficient extraction of landslide-related information. Experimental results demonstrate that the UNet-CBAM outperforms the baseline UNet by 10 % in performance over the UNet, with a notable improvement in the Area Under Curve (AUC) metric. The proposed model shows robustness in diverse and challenging landscapes, proving its effectiveness for landslide monitoring. This enhancement offers significant potential for improving early-warning systems, disaster preparedness, and risk management strategies in landslide-prone areas.