# D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2004.04788 · HTML: https://mlsanity.com/paper/2004.04788/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2020.

## Built on by (most cited first, 15 of 15)

- [Generate Your Own Scotland: Satellite Image Generation Conditioned on Maps](https://mlsanity.com/paper/2308.16648/) (2023)
- [Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education](https://mlsanity.com/paper/2309.10892/) (2023)
- [A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensing](https://mlsanity.com/paper/2110.01442/) (2021)
- [Platform-Independent and Curriculum-Oriented Intelligent Assistant for Higher Education](https://mlsanity.com/paper/2302.09294/) (2023)
- [Generative Adversarial Networks for Image Super-Resolution: A Survey](https://mlsanity.com/paper/2204.13620/) (2022)
- [DEM Super-Resolution with EfficientNetV2](https://mlsanity.com/paper/2109.09661/) (2021)
- [Single Image Super-Resolution Methods: A Survey](https://mlsanity.com/paper/2202.11763/) (2022)
- [TempNet -- Temporal Super Resolution of Radar Rainfall Products with Residual CNNs](https://mlsanity.com/paper/2109.09289/) (2021)
- [PlantPlotGAN: A Physics-Informed Generative Adversarial Network for Plant Disease Prediction](https://mlsanity.com/paper/2310.18268/) (2023)
- [High-resolution Multi-spectral Image Guided DEM Super-resolution using Sinkhorn Regularized Adversarial Network](https://mlsanity.com/paper/2311.16490/) (2023)
- [A Sinkhorn Regularized Adversarial Network for Image Guided DEM Super-resolution using Frequency Selective Hybrid Graph Transformer](https://mlsanity.com/paper/2409.14198/) (2024)
- [Stitching and dimensionality effects on large artificially generated volume datasets](https://mlsanity.com/paper/2606.20095/) (2026)
- [RoughNet: Mapping Arctic Sea Ice Roughness Using Diffusion-Based Super-Resolution of Satellite Imagery](https://mlsanity.com/paper/2607.13371/) (2026)
- [Guided Super-Resolution of Digital Elevation Models with Diffusion-Based Image Generators](https://mlsanity.com/paper/2609.11886/) (2026)
- [Efficient Continuous DEM Reconstruction under Limited Target-Resolution Supervision](https://mlsanity.com/paper/2609.29864/) (2026)

## Abstract (arXiv)

LIDAR (light detection and ranging) is an optical remote-sensing technique that measures the distance between sensor and object, and the reflected energy from the object. Over the years, LIDAR data has been used as the primary source of Digital Elevation Models (DEMs). DEMs have been used in a variety of applications like road extraction, hydrological modeling, flood mapping, and surface analysis. A number of studies in flooding suggest the usage of high-resolution DEMs as inputs in the applications improve the overall reliability and accuracy. Despite the importance of high-resolution DEM, many areas in the United States and the world do not have access to high-resolution DEM due to technological limitations or the cost of the data collection. With recent development in Graphical Processing Units (GPU) and novel algorithms, deep learning techniques have become attractive to researchers for their performance in learning features from high-resolution datasets. Numerous new methods have been proposed such as Generative Adversarial Networks (GANs) to create intelligent models that correct and augment large-scale datasets. In this paper, a GAN based model is developed and evaluated, inspired by single image super-resolution methods, to increase the spatial resolution of a given DEM dataset up to 4 times without additional information related to data.