Seamless High-Resolution Terrain Reconstruction: A Prior-Based Vision Transformer Approach

TL;DR

This paper introduces a novel prior-based vision transformer method for high-resolution terrain reconstruction from single-view imagery, significantly surpassing existing super-resolution techniques in resolution and accuracy.

Contribution

The work extends a monocular depth model to remote sensing, integrating low-res SRTM data with high-res NAIP imagery to produce globally consistent DEMs at 30 cm resolution.

Findings

Achieves 100x resolution enhancement over SRTM data.

Demonstrates less than 5 m mean absolute error compared to LiDAR.

Improves hydrological modeling accuracy for hazard assessment.

Abstract

High-resolution elevation data is essential for hydrological modeling, hazard assessment, and environmental monitoring; however, globally consistent, fine-scale Digital Elevation Models (DEMs) remain unavailable. Very high-resolution single-view imagery enables the extraction of topographic information at the pixel level, allowing the reconstruction of fine terrain details over large spatial extents. In this paper, we present single-view-based DEM reconstruction shown to support practical analysis in GIS environments across multiple sub-national jurisdictions. Specifically, we produce high-resolution DEMs for large-scale basins, representing a substantial improvement over the 30 m resolution of globally available Shuttle Radar Topography Mission (SRTM) data. The DEMs are generated using a prior-based monocular depth foundation (MDE) model, extended in this work to the remote sensing height domain for high-resolution, globally consistent elevation reconstruction. We fine-tune the model by integrating low-resolution SRTM data as a global prior with high-resolution RGB imagery from the National Agriculture Imagery Program (NAIP), producing DEMs with near LiDAR-level accuracy. Our method achieves a 100x resolution enhancement (from 30 m to 30 cm), exceeding existing super-resolution approaches by an order of magnitude. Across two diverse landscapes, the model generalizes robustly, resolving fine-scale terrain features with a mean absolute error of less than 5 m relative to LiDAR and improving upon SRTM by up to 18 %. Hydrological analyses at both catchment and hillslope scales confirm the method's utility for hazard assessment and environmental monitoring, demonstrating improved streamflow representation and catchment delineation. Finally, we demonstrate the scalability of the framework by applying it across large geographic regions.