  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.
