# Using Game Engines and Machine Learning to Create Synthetic Satellite Imagery for a Tabletop Verification Exercise

arXiv: https://arxiv.org/abs/2404.11461 · HTML: https://mlsanity.com/paper/2404.11461/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 17 Apr 2024.

## Builds on (most cited first, 8 of 8)

- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.08453/) (2023)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [RarePlanes: Synthetic Data Takes Flight](https://mlsanity.com/paper/2006.02963/) (2020)
- [Unity Perception: Generate Synthetic Data for Computer Vision](https://mlsanity.com/paper/2107.04259/) (2021)
- [NViSII: A Scriptable Tool for Photorealistic Image Generation](https://mlsanity.com/paper/2105.13962/) (2021)
- [The Synthinel-1 dataset: a collection of high resolution synthetic overhead imagery for building segmentation](https://mlsanity.com/paper/2001.05130/) (2020)
- [Generating Synthetic Satellite Imagery With Deep-Learning Text-to-Image Models -- Technical Challenges and Implications for Monitoring and Verification](https://mlsanity.com/paper/2404.07754/) (2024)

## Built on by (most cited first, 1 of 1)

- [Generating Synthetic Satellite Imagery for Rare Objects: An Empirical Comparison of Models and Metrics](https://mlsanity.com/paper/2409.01138/) (2024)

## Abstract (arXiv)

Satellite imagery is regarded as a great opportunity for citizen-based monitoring of activities of interest. Relevant imagery may however not be available at sufficiently high resolution, quality, or cadence -- let alone be uniformly accessible to open-source analysts. This limits an assessment of the true long-term potential of citizen-based monitoring of nuclear activities using publicly available satellite imagery. In this article, we demonstrate how modern game engines combined with advanced machine-learning techniques can be used to generate synthetic imagery of sites of interest with the ability to choose relevant parameters upon request; these include time of day, cloud cover, season, or level of activity onsite. At the same time, resolution and off-nadir angle can be adjusted to simulate different characteristics of the satellite. While there are several possible use-cases for synthetic imagery, here we focus on its usefulness to support tabletop exercises in which simple monitoring scenarios can be examined to better understand verification capabilities enabled by new satellite constellations and very short revisit times.