# NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations

arXiv: https://arxiv.org/abs/2011.07017 · HTML: https://mlsanity.com/paper/2011.07017/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2020.

## Built on by (most cited first, 4 of 4)

- [Can we integrate spatial verification methods into neural-network loss functions for atmospheric science?](https://mlsanity.com/paper/2203.11141/) (2022)
- [Creating synthetic night-time visible-light meteorological satellite images using the GAN method](https://mlsanity.com/paper/2108.04330/) (2021)
- [Simulating Nighttime Visible Satellite Imagery of Tropical Cyclones Using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2401.11679/) (2024)
- [SynWeather: Weather Observation Data Synthesis across Multiple Regions and Variables via a General Diffusion Transformer](https://mlsanity.com/paper/2511.08291/) (2025)

## Abstract (arXiv)

The recent explosion in applications of machine learning to satellite imagery often rely on visible images and therefore suffer from a lack of data during the night. The gap can be filled by employing available infra-red observations to generate visible images. This work presents how deep learning can be applied successfully to create those images by using U-Net based architectures. The proposed methods show promising results, achieving a structural similarity index (SSIM) up to 86% on an independent test set and providing visually convincing output images, generated from infra-red observations.