Cross-domain landslide mapping by harmonizing heterogeneous remote sensing datasets

Landslide mapping from remote sensing imagery is essential for disaster prevention and geohazard risk assessment. Recent advances in deep learning have enabled more accurate landslide extraction, yet current methods often suffer from limited generalization across regions with different imaging conditions, weak adaptability to diverse data sources, and difficulty in capturing small or subtle landslide features. To address these challenges, we propose a style-aware spatial-frequency spiking convolution (SSFSC) framework to enhance adaptability and extraction precision across heterogeneous domains using remote sensing images of varying spatial resolutions and sensors. SSFSC attempts to reduce domain shifts caused by variations in textures, color, and feature patterns across datasets by incorporating a style transfer strategy. On this basis, separable spiking convolution is proposed to mimic biologically inspired learning to capture the complex spectral and morphological features of landslides and aggregate distinctive landslide representations over background objects across spatial-frequency domains. To validate the effectiveness of SSFSC, we conduct experiments across diverse landslide-prone regions and compare with nine baseline methods, including DeepLabv3+, Segformer, TransUnet, Max-DeepLab, SwinUnet, Mask2Former, SCDUNet++, DAFormer and CLUDA. Results show that SSFSC significantly improves both accuracy and generalization, achieving an approximate 28.69% IoU improvement compared to existing methods. These findings demonstrate that SSFSC offers a scalable and efficient solution for automated landslide mapping, and has strong potential to support long-term hazard monitoring and emergency response applications under varying environmental conditions.