Guided Super-Resolution of Digital Elevation Models with Diffusion-Based Image Generators
On the map: Diffusion models → Inverse problems and image restoration
Abstract (arXiv)
High-resolution digital surface models (DSMs) play an important role in urban analysis, 3D building reconstruction, and infrastructure monitoring, yet their availability remains limited due to the high cost and complexity of data acquisition. In contrast, coarse DSMs from commercial satellite missions are widely accessible, and high-resolution optical imagery is increasingly available from aerial and satellite platforms. We address the resulting mismatch in spatial resolution and propose a DSM superresolution approach that enhances 5 m DSMs to 0.5 m resolution, using guidance from high-resolution spectral images. Our method employs denoising diffusion to transfer information that is visible only in the image, like crisp outlines and detailed roof structures, into the elevation maps. In this way, surface details are reconstructed more accurately than with conventional interpolation or filtering techniques. Experiments on several cities in Central Europe demonstrate that the proposed approach produces high-quality DSMs with improved structural detail and accurate surface geometry. Our results highlight the potential of guided super-resolution with foundational image priors as a means of reconstructing high-resolution surface models.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models. Builds on 15 of its 36 references that are in the map. First posted on arXiv 10 Sep 2026.
Builds on
15 of 36 references.
References · 15
- 2020Denoising Diffusion Probabilistic Modelscited by 21,076
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2020Denoising Diffusion Implicit Modelscited by 9,781
- 2024Scaling Rectified Flow Transformers for High-Resolution Image Synthesiscited by 4,181
- 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,700
- 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,908
- 2017DOTA: A Large-scale Dataset for Object Detection in Aerial Imagescited by 623
- 2023Repurposing Diffusion-Based Image Generators for Monocular Depth Estimationcited by 327
- 2024GeoWizard: Unleashing the Diffusion Priors for 3D Geometry Estimation from a Single Imagecited by 286
- 2021Object Detection in Aerial Images: A Large-Scale Benchmark and Challengescited by 204
- 2024Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusioncited by 50
- 2022Guided Depth Super-Resolution by Deep Anisotropic Diffusioncited by 46
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- 2026Enhancing Photogrammetric Digital Surface Models with Pretrained Diffusion Models and Multimodal Conditioning
- 2025Dfilled: Repurposing Edge-Enhancing Diffusion for Guided DSM Void Filling
- 2023Single-View Height Estimation with Conditional Diffusion Probabilistic Models
- 2024Building Bridges across Spatial and Temporal Resolutions: Reference-Based Super-Resolution via Change Priors and Conditional Diffusion Model
- 2025A Diffusion-Based Framework for Terrain-Aware Remote Sensing Image Reconstruction
- 2024Efficient Denoising Method to Improve The Resolution of Satellite Images
- 2025Seeing Through the Rain: Resolving High-Frequency Conflicts in Deraining and Super-Resolution via Diffusion Guidance
- 2026When Earth Foundation Models Meet Diffusion: An Application to Land Surface Temperature Super-Resolution