  In this paper, we demonstrated a practical application of realistic river
image generation using deep learning. Specifically, we explored a generative
adversarial network (GAN) model capable of generating high-resolution and
realistic river images that can be used to support modeling and analysis in
surface water estimation, river meandering, wetland loss, and other
hydrological research studies. First, we have created an extensive repository
of overhead river images to be used in training. Second, we incorporated the
Progressive Growing GAN (PGGAN), a network architecture that iteratively trains
smaller-resolution GANs to gradually build up to a very high resolution to
generate high quality (i.e., 1024x1024) synthetic river imagery. With simpler
GAN architectures, difficulties arose in terms of exponential increase of
training time and vanishing/exploding gradient issues, which the PGGAN
implementation seemed to significantly reduce. The results presented in this
study show great promise in generating high-quality images and capturing the
details of river structure and flow to support hydrological research, which
often requires extensive imagery for model performance.
