# Realistic River Image Synthesis using Deep Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2003.00826 · HTML: https://mlsanity.com/paper/2003.00826/

## Key facts

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

## Built on by (most cited first, 9 of 9)

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- [TempNet -- Temporal Super Resolution of Radar Rainfall Products with Residual CNNs](https://mlsanity.com/paper/2109.09289/) (2021)
- [Modelling nonlinear dependencies in the latent space of inverse scattering](https://mlsanity.com/paper/2203.10307/) (2022)
- [Artificial ASMR: A Cyber-Psychological Approach](https://mlsanity.com/paper/2210.14321/) (2022)

## Abstract (arXiv)

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.