Summary of Using Game Engines and Machine Learning to Create Synthetic Satellite Imagery For a Tabletop Verification Exercise, by Johannes Hoster et al.
Using Game Engines and Machine Learning to Create Synthetic Satellite Imagery for a Tabletop Verification Exercise
by Johannes Hoster, Sara Al-Sayed, Felix Biessmann, Alexander Glaser, Kristian Hildebrand, Igor Moric, Tuong Vy Nguyen
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper proposes a novel approach to generate synthetic satellite imagery using modern game engines and machine-learning techniques. The authors demonstrate how this method allows for the creation of high-resolution, high-quality, and customizable images of sites of interest, including variables like time of day, cloud cover, season, and level of activity onsite. The application of synthetic imagery is focused on supporting tabletop exercises in monitoring scenarios, enabling the evaluation of verification capabilities enabled by new satellite constellations and short revisit times. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Synthetic satellite imagery can be used to support citizen-based monitoring of nuclear activities using publicly available satellite imagery. This method can help assess the true long-term potential of this type of monitoring. The authors show how game engines combined with machine-learning techniques can generate synthetic images that can simulate different characteristics of satellites, such as resolution and off-nadir angle. |
Keywords
» Artificial intelligence » Machine learning
