A Dual-Task Optimization Approach for Digital Elevation Model Correction with Spaceborne LiDAR Data

Abstract. Digital Elevation Models (DEMs) are essential for terrain analysis and environmental applications, yet freely available global DEMs such as the Shuttle Radar Topography Mission (SRTM) DEM often contain noticeable elevation errors. Recent advances in space-borne LiDAR, particularly Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), provide highly accurate elevation observations for DEM correction. However, most existing studies treat DEM correction as a single regression task and pay limited attention to correction direction, although direction errors may further degrade the corrected DEM. To address this issue, this study proposes a dual-task optimization framework for DEM correction using ICESat-2 data and auxiliary topographic and environmental variables. The network includes a shared feature extraction backbone, a regression branch for estimating correction values, and a classification branch for predicting whether DEM elevation should be increased or decreased. Kent County, New Brunswick, Canada, was selected as the study area, where 35,823 ICESat-2 elevation points were used for model training and validation. Results show that the proposed method outperforms Random Forest, XGBoost, and a conventional deep neural network, achieving a root mean square error (RMSE) of 1.76 m, a mean absolute error (MAE) of 1.37 m, and a direction consistency rate (DCR) of 75.05%. Compared with the original SRTM DEM, the corrected DEM reduces RMSE and MAE by approximately 27.6% and 25.9%, respectively, and improves DCR by 1.66% over the conventional deep neural network (DNN). These results demonstrate that incorporating correction direction into the learning process can effectively improve DEM correction accuracy and directional reliability.