TerrainGraphNet: Terrain-Constrained Graph Reasoning for Landslide Segmentation

Accurate landslide segmentation from high-resolution remote sensing imagery remains challenging due to weak spectral contrast and strong dependence on terrain structure. Unlike appearance-defined objects, landslides follow geomorphological patterns governed by slope continuity and elevation discontinuities. However, most existing segmentation methods treat landslides as generic semantic regions and incorporate Digital Elevation Models (DEM) through simple feature fusion without explicitly enforcing terrain-consistent spatial reasoning. To address this limitation, we reformulate landslide segmentation as a terrainconditioned structured prediction problem and propose TerrainGraphNet, a terrain-aware graph reasoning framework that embeds geomorphological constraints into representation learning and spatial propagation. The proposed method introduces terrain-modulated feature interaction, where elevation structure adaptively regulates visual representations, and terrain-aware graph construction, where spatial connectivity is defined jointly by feature similarity and slope continuity. This formulation can be interpreted as learning a terrain-weighted smoothness prior that encourages predictions to respect geomorphological coherence. Extensive experiments on three benchmark datasets demonstrate consistent improvements over strong CNN and transformer baselines. In addition to higher IoU and F1 scores, TerrainGraphNet significantly improves boundary accuracy and topological consistency, highlighting the importance of terrain-constrained reasoning for reliable landslide delineation.