Terrain-Informed Landslide Learning for Spatially Explicit Landslide Prediction

Landslide prediction from remote sensing data is commonly formulated as a pixel-level segmentation problem, where accurate delineation requires the integration of spectral, terrain, and spatial information across multiple scales. Existing deep learning models can capture these representations effectively, but improvements in prediction accuracy are often accompanied by increased model complexity and computational cost. To address this accuracy–complexity trade-off, we propose TILL (Terrain-Informed Landslide Learning), a terrain-aware framework for spatially explicit landslide prediction. TILL integrates Band-Group Attention and a Terrain-Gated Encoder to jointly model spectral groups and terrain characteristics, followed by Multi-Scale Feature Aggregation and decoding to preserve spatially detailed representations. A Recurrent Refinement Unit subsequently refines the initial prediction using the learned features and prediction state. This design combines terrain-aware encoding, multi-scale representation, and recurrent prediction refinement within a unified segmentation pipeline. TILL produces pixel-level landslide probability maps with an accuracy of 0.85 using 33M parameters and 38G FLOPs. TILL is trained and evaluated on the Landslide4Sense dataset, which is specially designed for landslide detection. Compared with the average performance of the evaluated methods, TILL has a better accuracy-complexity tradeoff, with 9.68% improvement on accuracy, 7.69% fewer parameters, and 1.94% fewer FLOPs. The results show that TILL provides a considerable balance between landslide prediction accuracy and model complexity for spatially explicit delineation.