DSM-to-DTM Reconstruction Using Only DSM-Derived Inputs with Residual Learning and CSF Priors

Digital terrain models (DTMs) are required in many hydrologic, geomorphic, and ecological applications, yet widely used global elevation products often retain above-ground elevation contributions, particularly from vegetation canopies. This study investigates whether useful bare-earth terrain can be reconstructed from DSM-derived information alone at inference time. Rather than regressing terrain elevation directly, the proposed framework predicts the residual DH=DSM−DTM and reconstructs the DTM by subtraction. The model uses Copernicus DEM GLO-30 as the input source and augments it with CSF-derived priors and DSM-derived terrain features, including slope, aspect encoding, curvature, and local relief. Unlike multi-source terrain correction products that rely on external auxiliary datasets, all inference-time inputs in the proposed framework are generated from the DSM itself. A residual U-Net is trained with a weighted Huber loss together with gradient-consistency and DTM-slope-consistency constraints. Experiments across multiple regions in the central and southeastern United States show that the proposed method outperforms the compared public DEM products and baseline methods under a unified evaluation protocol. Relative to FathomDEM, it reduces the mean absolute error from 1.0445 m to 0.8538 m and the root mean square error from 1.6969 m to 1.4697 m on the study region test split, while also improving NMAD, P99, and Recall@5m. Performance on the geographically separate Arkansas region is similar to that on the in-region test split. Remaining errors are concentrated mainly in extremely steep terrain, densely vegetated areas, and cases with large residual heights.