Terrain-Aware Attention Network for DEM Terrain Semantic Segmentation

Terrain segmentation is important for semantic extraction and scene understanding in geographic information systems. However, generic deep learning models lack mechanisms tailored to the physical characteristics of terrain data. We introduce the Terrain-Aware Attention (TAA) module, which uses terrain cues---local gradient, global elevation context, and surface ruggedness---to guide the attention mechanism for terrain-specific segmentation. We also propose the Hierarchical Asymmetric Attention Mechanism (HAAM), which assigns different attention modules to different network depths. We integrate TAA and HAAM into a U-Net architecture and evaluate them on a DEM dataset derived from ALOS PALSAR imagery. Experiments show that both TAA and HAAM independently improve segmentation performance over the baseline, and their combination achieves the highest IoU among all tested configurations.