DEM super-resolution guided by high-resolution remote sensing images using multitask learning

High-resolution digital elevation models (DEMs) are critical for applications such as environmental monitoring and urban planning, motivating the development of advanced DEM super-resolution (SR) techniques. While recent methods have shown promising results, effectively exploiting high-resolution remote sensing images (HRSIs) to guide DEM SR remains challenging, and progress has been hindered by the lack of large-scale, open-source benchmark datasets. We propose GSRMTL, a novel and parameter-efficient multi-task learning framework for HRSI-guided DEM SR. Given a low-resolution DEM and a paired HRSI, GSRMTL jointly performs DEM SR and semantic segmentation of the optical imagery, where segmentation acts as an auxiliary task to provide semantic priors for elevation reconstruction. To address the dataset bottleneck, we introduce GDEMSR, the first large-scale benchmark dataset specifically designed for HRSI-guided DEM SR. Extensive experiments on GDEMSR and the RGB-guided depth SR benchmark NYU-v2 demonstrate that GSRMTL consistently outperforms state-of-the-art methods while using significantly fewer parameters, highlighting its effectiveness and practical deployment potential. • GSRMTL: an attention-based multi-task model for HRSI-guided DEM SR. • GDEMSR: the first open dataset for benchmarking HRSI-guided DEM SR. • GSRMTL achieves SOTA results, with code and data to be publicly released.