DEM-Assisted Topography-Conditioned and Orientation-Adaptive Siamese Network for Cross-Region Landslide Change Detection

Automated landslide change detection using remote sensing imagery is critical for rapid disaster response. However, landslide change detection using bi-temporal optical imagery is frequently degraded by cross-region domain shifts and by the elongated, anisotropic morphology of landslide boundaries, leading to substantial pseudo-change alarms. To suppress pseudo-changes and improve cross-region robustness, we propose a DEM-assisted topography-conditioned and orientation-adaptive Siamese network (DEMO-Net) that injects topographic inductive bias through terrain-conditioned feature modulation and orientation-adaptive convolutions. Specifically, DEM-derived multi-channel priors are encoded to predict spatially varying FiLM parameters that recalibrate shallow optical features, suppressing spurious changes while preserving discriminative cues. In addition, we introduce an adaptive-oriented attention convolution that leverages a DEM-derived aspect to guide sparse multi-orientation aggregation via shared-kernel transformation, enabling direction-aware receptive-field alignment for elongated and direction-varying landslide structures without costly global attention. Experiments on the GVLM benchmark under a 5-fold site-wise cross-region protocol show that DEMO-Net achieves 85.17% F1 and 74.26% mIoU, outperforming the strongest CNN baseline FC-EF by 5.05% and 7.20%, respectively. These results demonstrate the effectiveness of jointly leveraging terrain-conditioned calibration and physically consistent orientation-aligned feature extraction for robust cross-region landslide change detection.