# Generating Synthetic Multispectral Satellite Imagery from Sentinel-2

arXiv: https://arxiv.org/abs/2012.03108 · HTML: https://mlsanity.com/paper/2012.03108/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2020.

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

- [Generating Physically-Consistent Satellite Imagery for Climate Visualizations](https://mlsanity.com/paper/2104.04785/) (2021)
- [A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensing](https://mlsanity.com/paper/2110.01442/) (2021)
- [SatDM: Synthesizing Realistic Satellite Image with Semantic Layout Conditioning using Diffusion Models](https://mlsanity.com/paper/2309.16812/) (2023)
- [Generation of the NIR spectral Band for Satellite Images with Convolutional Neural Networks](https://mlsanity.com/paper/2106.07020/) (2021)
- [Multispectral Texture Synthesis using RGB Convolutional Neural Networks](https://mlsanity.com/paper/2410.16019/) (2024)

## Abstract (arXiv)

Multi-spectral satellite imagery provides valuable data at global scale for many environmental and socio-economic applications. Building supervised machine learning models based on these imagery, however, may require ground reference labels which are not available at global scale. Here, we propose a generative model to produce multi-resolution multi-spectral imagery based on Sentinel-2 data. The resulting synthetic images are indistinguishable from real ones by humans. This technique paves the road for future work to generate labeled synthetic imagery that can be used for data augmentation in data scarce regions and applications.