# Digital Elevation Model Estimation from RGB Satellite Imagery using Generative Deep Learning

arXiv: https://arxiv.org/abs/2511.21985 · HTML: https://mlsanity.com/paper/2511.21985/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 26 Nov 2025.

## Builds on (most cited first, 3 of 3)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Void Filling of Digital Elevation Models with Deep Generative Models](https://mlsanity.com/paper/1811.12693/) (2018)
- [Progressive Cross Attention Network for Flood Segmentation using Multispectral Satellite Imagery](https://mlsanity.com/paper/2501.11923/) (2025)

## Built on by (most cited first, 1 of 1)

- [LunarDepthNet: Generation of Digital Elevation Models using Deep Learning and Monocular Satellite Images](https://mlsanity.com/paper/2604.22848/) (2026)

## Abstract (arXiv)

Digital Elevation Models (DEMs) are vital datasets for geospatial applications such as hydrological modeling and environmental monitoring. However, conventional methods to generate DEM, such as using LiDAR and photogrammetry, require specific types of data that are often inaccessible in resource-constrained settings. To alleviate this problem, this study proposes an approach to generate DEM from freely available RGB satellite imagery using generative deep learning, particularly based on a conditional Generative Adversarial Network (GAN). We first developed a global dataset consisting of 12K RGB-DEM pairs using Landsat satellite imagery and NASA's SRTM digital elevation data, both from the year 2000. A unique preprocessing pipeline was implemented to select high-quality, cloud-free regions and aggregate normalized RGB composites from Landsat imagery. Additionally, the model was trained in a two-stage process, where it was first trained on the complete dataset and then fine-tuned on high-quality samples filtered by Structural Similarity Index Measure (SSIM) values to improve performance on challenging terrains. The results demonstrate promising performance in mountainous regions, achieving an overall mean root-mean-square error (RMSE) of 0.4671 and a mean SSIM score of 0.2065 (scale -1 to 1), while highlighting limitations in lowland and residential areas. This study underscores the importance of meticulous preprocessing and iterative refinement in generative modeling for DEM generation, offering a cost-effective and adaptive alternative to conventional methods while emphasizing the challenge of generalization across diverse terrains worldwide.