Void filling of digital elevation models based on terrain feature-guided diffusion model

Digital Elevation Models (DEMs) are pivotal in scientific research and engineering because they provide essential topographic and geomorphological information. Voids in DEM data result in the loss of terrain information, significantly impacting its broad applicability. Although spatial interpolation methods are frequently employed to address these voids, they suffer from accuracy degradation and struggle to reconstruct intricate terrain features. Generative Adversarial Network (GAN)-based approaches have emerged as promising solutions to enhance elevation accuracy and facilitate the reconstruction of partial terrain features. Nonetheless, GAN-based methods exhibit limitations with specific void shapes, and their performance is susceptible to artifacts and elevation jumps around the void boundaries. To address shortcomings mentioned above, we propose a terrain feature-guided diffusion model (TFDM) to fill the DEM data voids. The training and inference processes of the diffusion model were constrained by terrain feature lines to ensure the stability of the generated DEM surface. The TFDM is distinguished by its ability to generate seamless DEM surfaces and maintain stable terrain contours in response to varying terrain conditions. Experiments were conducted to validate the applicability of TFDM using different DEMs, including Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Models (ASTER GDEMv3) and the TanDEM-X global DEM. The proposed TFDM algorithm and comparison methods such as DDPM, GAN, and Kriging were applied to a full test set of 271 DEM images covering different terrain environments. The mean absolute error (MAE) and root mean square error (RMSE) of the DEM restored by TFDM were 28.91 ± 9.45 m and 38.16 ± 13.00 m, respectively, while the MAE and RMSE of the comparison algorithms were no less than 60.87 ± 26.24 m and 82.80 ± 36.51 m or even higher, validating the effectiveness of the TFDM algorithm in filling DEM voids. Profile analysis in partial details indicates that the TFDM outperforms alternative methods in reconstructing terrain features, as confirmed through visual inspection and quantitative comparison. TFDM exhibits versatility when applied to DEM data with diverse resolutions and produced using various measurement techniques. •A Terrain Feature-Guided Diffusion Model (TFDM) is developedto fill DEM data voids.•Terrain features guide diffusion model to learn real DEM distribution.•TFDM can generateseam