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.

翻译：卫星影像被视为基于公众参与的兴趣活动监测的重要机遇。然而，相关影像可能无法以足够高的分辨率、质量或重访频率获取——更遑论对开源分析人员实现普遍可及。这限制了对使用公开卫星影像进行核活动公众监测之长期潜力的真实评估。本文展示了如何将现代游戏引擎与先进机器学习技术相结合，用以按需生成具有可调参数的兴趣地点合成影像；这些参数包括昼夜时段、云量、季节或现场活动水平。同时，可通过调整分辨率和离天底角来模拟不同卫星的特性。尽管合成影像存在多种潜在应用场景，本文重点探讨其在支持桌面演练中的效用——通过检验简单监测场景，以更好地理解由新型卫星星座和极短重访周期所实现的核查能力。