Tang et al. (2026) High-Resolution Optical Satellite Image Guided DEM Super-Resolution via Topographic-Aware Transformer

Identification

• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Yubin Tang, Enping Yan, Yujiu Xiong, Jiawei Jiang, Hua Sun, Dengkui Mo
• DOI: 10.1109/tgrs.2026.3677203

Research Groups

[Not specified in the provided text.]

Short Summary

This paper introduces a novel topographic-aware transformer model that utilizes high-resolution optical satellite images to achieve super-resolution of Digital Elevation Models (DEMs).

Objective

• To develop and evaluate a deep learning architecture, specifically a topographic-aware transformer, for enhancing the spatial resolution of Digital Elevation Models (DEMs) by leveraging guidance from high-resolution optical satellite imagery.

Study Configuration

• Spatial Scale: Local to regional scale, focusing on improving the detailed representation of terrain features.
• Temporal Scale: Not explicitly defined, but typically involves static or near-static terrain data for DEM generation and super-resolution.

Methodology and Data

• Models used: Topographic-Aware Transformer
• Data sources: High-resolution optical satellite images, Digital Elevation Models (DEMs)

Main Results

[Not specified in the provided text.]

Contributions

[Not specified in the provided text, beyond the general novelty implied by the title.]

Funding

[Not specified in the provided text.]

Citation

@article{Tang2026HighResolution,
  author = {Tang, Yubin and Yan, Enping and Xiong, Yujiu and Jiang, Jiawei and Sun, Hua and Mo, Dengkui},
  title = {High-Resolution Optical Satellite Image Guided DEM Super-Resolution via Topographic-Aware Transformer},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2026},
  doi = {10.1109/tgrs.2026.3677203},
  url = {https://doi.org/10.1109/tgrs.2026.3677203}
}