# DEM Super-Resolution with EfficientNetV2

arXiv: https://arxiv.org/abs/2109.09661 · HTML: https://mlsanity.com/paper/2109.09661/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 34 references that are in the map.
- First posted on arXiv 20 Sep 2021.

## Builds on (most cited first, 20 of 20)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [Accelerating the Super-Resolution Convolutional Neural Network](https://mlsanity.com/paper/1608.00367/) (2016)
- [Deep Learning for Image Super-resolution: A Survey](https://mlsanity.com/paper/1902.06068/) (2019)
- [Deeply-Recursive Convolutional Network for Image Super-Resolution](https://mlsanity.com/paper/1511.04491/) (2015)
- [Tackling Climate Change with Machine Learning](https://mlsanity.com/paper/1906.05433/) (2019)
- [A Fully Progressive Approach to Single-Image Super-Resolution](https://mlsanity.com/paper/1804.02900/) (2018)
- [A Comprehensive Review of Deep Learning Applications in Hydrology and Water Resources](https://mlsanity.com/paper/2007.12269/) (2020)
- [D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks](https://mlsanity.com/paper/2004.04788/) (2020)
- [Short-term Hourly Streamflow Prediction with Graph Convolutional GRU Networks](https://mlsanity.com/paper/2107.07039/) (2021)

## Built on by (most cited first, 5 of 5)

- [Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education](https://mlsanity.com/paper/2309.10892/) (2023)
- [Performance of ChatGPT on the US Fundamentals of Engineering Exam: Comprehensive Assessment of Proficiency and Potential Implications for Professional Environmental Engineering Practice](https://mlsanity.com/paper/2304.12198/) (2023)
- [Platform-Independent and Curriculum-Oriented Intelligent Assistant for Higher Education](https://mlsanity.com/paper/2302.09294/) (2023)
- [High-resolution Multi-spectral Image Guided DEM Super-resolution using Sinkhorn Regularized Adversarial Network](https://mlsanity.com/paper/2311.16490/) (2023)
- [A Sinkhorn Regularized Adversarial Network for Image Guided DEM Super-resolution using Frequency Selective Hybrid Graph Transformer](https://mlsanity.com/paper/2409.14198/) (2024)

## Abstract (arXiv)

Efficient climate change monitoring and modeling rely on high-quality geospatial and environmental datasets. Due to limitations in technical capabilities or resources, the acquisition of high-quality data for many environmental disciplines is costly. Digital Elevation Model (DEM) datasets are such examples whereas their low-resolution versions are widely available, high-resolution ones are scarce. In an effort to rectify this problem, we propose and assess an EfficientNetV2 based model. The proposed model increases the spatial resolution of DEMs up to 16times without additional information.