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

arXiv: https://arxiv.org/abs/2507.09681 · HTML: https://mlsanity.com/paper/2507.09681/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 64 references that are in the map.
- First posted on arXiv 13 Jul 2025.

## Builds on (most cited first, 13 of 13)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [MiDaS v3.1 -- A Model Zoo for Robust Monocular Relative Depth Estimation](https://mlsanity.com/paper/2307.14460/) (2023)
- [Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction](https://mlsanity.com/paper/1803.03893/) (2018)
- [Prompting Depth Anything for 4K Resolution Accurate Metric Depth Estimation](https://mlsanity.com/paper/2412.14015/) (2024)
- [Monocular Depth Estimation Based On Deep Learning: An Overview](https://mlsanity.com/paper/2003.06620/) (2020)
- [Depth Any Canopy: Leveraging Depth Foundation Models for Canopy Height Estimation](https://mlsanity.com/paper/2408.04523/) (2024)
- [Towards Robust Monocular Depth Estimation in Non-Lambertian Surfaces](https://mlsanity.com/paper/2408.06083/) (2024)
- [Self-Supervised Learning for Monocular Depth Estimation from Aerial Imagery](https://mlsanity.com/paper/2008.07246/) (2020)
- [TanDepth: Leveraging Global DEMs for Metric Monocular Depth Estimation in UAVs](https://mlsanity.com/paper/2409.05142/) (2024)
- [Enhancing Monocular Height Estimation via Sparse LiDAR-Guided Correction](https://mlsanity.com/paper/2505.06905/) (2025)
- [Real-GDSR: Real-World Guided DSM Super-Resolution via Edge-Enhancing Residual Network](https://mlsanity.com/paper/2404.03930/) (2024)

## Abstract (arXiv)

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.