JSPSR: Joint Spatial Propagation Super-Resolution Networks for Enhancement of Bare-Earth Digital Elevation Models from Global Data
Xiandong Cai, Matthew D. Wilson · Remote Sensing · 2025
(1) Background: Digital Elevation Models (DEMs) encompass digital bare earth surface representations that are essential for spatial data analysis, such as hydrological and geological modelling, as well as for other applications, such as agriculture and environmental management. However, available bare-earth DEMs can have limited coverage or accessibility. Moreover, the majority of available global DEMs have lower spatial resolutions (∼30–90 m) and contain errors introduced by surface features such as buildings and vegetation. (2) Methods: This research presents an innovative method to convert global DEMs to bare-earth DEMs while enhancing their spatial resolution as measured by the improved vertical accuracy of each pixel, combined with reduced pixel size. We propose the Joint Spatial Propagation Super-Resolution network (JSPSR), which integrates Guided Image Filtering (GIF) and Spatial Propagation Network (SPN). By leveraging guidance features extracted from remote sensing images with or without auxiliary spatial data, our method can correct elevation errors and enhance the spatial resolution of DEMs. We developed a dataset for real-world bare-earth DEM Super-Resolution (SR) problems in low-relief areas utilising open-access data. Experiments were conducted on the dataset using JSPSR and other methods to predict 3 m and 8 m spatial resolution DEMs from 30 m spatial resolution Copernicus GLO-30 DEMs. (3) Results: JSPSR improved prediction accuracy by 71.74% on Root Mean Squared Error (RMSE) and reconstruction quality by 22.9% on Peak Signal-to-Noise Ratio (PSNR) compared to bicubic interpolated GLO-30 DEMs, and achieves 56.03% and 13.8% improvement on the same items against a baseline Single Image Super Resolution (SISR) method. Overall RMSE was 1.06 m at 8 m spatial resolution and 1.1 m at 3 m, compared to 3.8 m for GLO-30, 1.8 m for FABDEM and 1.3 m for FathomDEM, at either resolution. (4) Conclusions: JSPSR outperforms other methods in bare-earth DEM super-resolution tasks, with improved elevation accuracy compared to other state-of-the-art globally available datasets.