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

TL;DR

This paper introduces D-SRGAN, a GAN-based method inspired by super-resolution techniques, to enhance the spatial resolution of DEM datasets up to four times without extra data, addressing data scarcity issues.

Contribution

The paper presents a novel GAN model specifically designed for DEM super-resolution, improving resolution without requiring additional data sources.

Findings

D-SRGAN effectively quadruples DEM resolution.

The method outperforms traditional interpolation techniques.

High-resolution DEMs improve accuracy in flood mapping and surface analysis.

Abstract

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.