A new computational model allows researchers to draw on normally incompatible data sets, such as satellite imagery and social media posts, to answer questions about what is happening in targeted locations. The researchers developed the model to serve as a tool for identifying violations of nuclear nonproliferation agreements.
“Our goal was to develop a working framework that uses information from a variety of sensors and data sources to identify these potential violations of nuclear nonproliferation,” says Hamid Krim, co-author of a paper on the work, a professor of electrical and computer engineering at North Carolina State University and director of the Vision, Information, and Statistical Signal Theories and Applications Group (VISSTA) Laboratory.
“Some of these data may be conventional, such as Geiger counter readings or multispectral data from satellite imagery. But many of these data sources may be nontraditional, such as social media posts. And these sources provide a wide variety of data that are not normally compatible, such as the text included on Twitter posts and the images posted on Flickr. By making these different inputs compatible with each other, we are able to accept a broader range of data inputs and use that data in a meaningful way that, ultimately, can help authorities reach more reliable conclusions,” Krim says.
The researchers say the model can be used to work with any data that can be identified as coming from the targeted area. For example, satellite images are clearly identifiable, but they may also draw on social media posts that are actively or passively tagged as coming from the relevant area.
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