RSiDF-T: Remote Sensing image-guided Diffusion Filling with Topology regularization for SRTM Data Restoration

This is the official code of RSiDF-T: Remote Sensing Image-Guided Diffusion Filling with Topology Regularization for SRTM Restoration over Complex Terrains. We propose a conditional diffusion framework (RSiDF-T) for restoring SRTM DEM voids over complex terrains. The method adopts a coarse-to-fine two-stage reconstruction scheme. In the coarse stage, we design an optical-guided contextual attention module (OCAM) to leverage texture and structural cues from co-registered Sentinel-2 optical imagery and construct an initial elevation-structure prior for void regions. In the refinement stage, a conditional diffusion model progressively reconstructs high-fidelity elevations within gaps, assisted by a topology-aware regularization term to reduce boundary artifacts and preserve key topographic structures such as ridges and valleys.The SRTM DEM data used in this study are publicly available from the U.S. Geological Survey EarthExplorer. Sentinel-2 imagery used in this study is publicly available from the Copernicus Data Space Ecosystem.