Abstract
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.
Abstract (translated)
卫星图像被视为公民基于监测感兴趣活动的重要机会。然而，相关图像可能不会在足够高的分辨率、质量或频率上可用，更不用说让开源分析师轻松访问了。这限制了对使用公共卫星图像进行公民基于监测的长期潜在能力的评估。在本文中，我们展示了如何将现代游戏引擎与先进机器学习技术相结合，生成具有按请求选择相关参数的感兴趣地点的合成图像。这些参数包括时间、云层、季节或现场活动水平。同时，分辨率和服务角可以调整以模拟卫星的不同特性。虽然合成图像有多种可能的用例，但在这里我们关注其对桌面练习的支持，这些练习可以用来更好地了解新卫星星座及其非常短的重新访问时间所能带来的验证能力。
URL
https://arxiv.org/abs/2404.11461
