# Feedback Neural Network based Super-resolution of DEM for generating high fidelity features

arXiv: https://arxiv.org/abs/2007.01940 · HTML: https://mlsanity.com/paper/2007.01940/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2020.

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

- [Deep Generative Framework for Interactive 3D Terrain Authoring and Manipulation](https://mlsanity.com/paper/2201.02369/) (2022)

## Abstract (arXiv)

High resolution Digital Elevation Models(DEMs) are an important requirement for many applications like modelling water flow, landslides, avalanches etc. Yet publicly available DEMs have low resolution for most parts of the world. Despite tremendous success in image super resolution task using deep learning solutions, there are very few works that have used these powerful systems on DEMs to generate HRDEMs. Motivated from feedback neural networks, we propose a novel neural network architecture that learns to add high frequency details iteratively to low resolution DEM, turning it into a high resolution DEM without compromising its fidelity. Our experiments confirm that without any additional modality such as aerial images(RGB), our network DSRFB achieves RMSEs of 0.59 to 1.27 across 4 different datasets.