Satellite image of vehicles observed at the Yongbyon rail station, near the Yongbyon nuclear complex. Image courtesy Airbus Pleiades/Orbital Insight

Machine learning improves satellite imagery analysis of North Korean nuclear activity

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Satellite image of vehicles observed at the Yongbyon rail station, near the Yongbyon nuclear complex. Image courtesy Airbus Pleiades/Orbital Insight

In the midst of what has been termed the “new space race,” a few hundred to tens of thousands of satellites are being approved by the Federal Communications Commission for launch into Earth’s orbit (Venkatesan et al 2020). As of 2021, there are 3,372 operational satellites encircling the planet (Therese 2021). Within a decade, there will be close to 100,000 satellites encircling the Earth (Venkatesan at al 2020). At the same time, remote sensing technology has rapidly developed in terms of quality and applications. Close to 30 percent of all currently operational satellites perform Earth observation missions for applications as diverse as environmental monitoring and border security (Therese 2021).

The utility of these emerging capabilities extends to the arena of international security. For decades, the International Atomic Energy Agency has used satellite imagery to monitor illicit or undeclared nuclear activities as part of its nuclear nonproliferation function (Pabian 2012). Commercial satellite imagery has further enabled nongovernmental organizations and individuals to detect and monitor activities in remote and inaccessible areas, contributing to the rise of open-source intelligence (Park et al 2020). Many institutions use open-source imagery to corroborate, validate, or refute governmental data, improving both the accessibility and rigor of international security analyses.

As the Russian invasion of Ukraine shows, nuclear threats are real, present, and dangerous

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