Journal article
D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks
SN computer science, Vol.2(1)
01/20/2021
DOI: 10.1007/s42979-020-00442-2
Abstract
Digital elevation model (DEM) is a critical data source for variety of applications such as road extraction, hydrological modeling, flood mapping, and many geospatial studies. The usage of high-resolution DEMs as inputs in many application areas improves the overall reliability and accuracy of the raw dataset. The goal of this study is to develop a machine learning model that increases the spatial resolution of DEM without additional information. In this paper, a GAN based model (D-SRGAN), inspired by single image super-resolution methods, is developed and evaluated to increase the resolution of DEMs. The experiment results show that D-SRGAN produces promising results while constructing 3 feet high-resolution DEMs from 50 feet low-resolution DEMs. It outperforms common statistical interpolation methods and neural network algorithms.This study shows that it is possible to use the power of artificial neural networks to increase the resolution of the DEMs. The study also demonstrates that approaches from single image super-resolution can be applied for DEM super-resolution.
Details
- Title: Subtitle
- D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks
- Creators
- Bekir Z Demiray - University of IowaMuhammed Sit - University of IowaIbrahim Demir - University of Iowa
- Resource Type
- Journal article
- Publication Details
- SN computer science, Vol.2(1)
- DOI
- 10.1007/s42979-020-00442-2
- ISSN
- 2662-995X
- eISSN
- 2661-8907
- Publisher
- Springer Singapore
- Language
- English
- Date published
- 01/20/2021
- Academic Unit
- Electrical and Computer Engineering; Civil and Environmental Engineering; IIHR--Hydroscience and Engineering; Injury Prevention Research Center
- Record Identifier
- 9984202144502771
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