Using Game Engines and Machine Learning to Create Synthetic Satellite
  Imagery for a Tabletop Verification Exercise
Johannes Hoster Sara Al-Sayed Felix Biessmann Alexander Glaser Kristian Hildebrand Igor Moric Tuong Vy Nguyen
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.