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The volume of GEOINT data continues to grow, while analysts struggle with the volume, variety, and velocity of space-based data. Many space and airborne sensors today can provide imagery suitable for geographical intelligence. A new technology will demonstrate that GEOINT gleaned through data fusion is greater than the simple sum of GEOINT gleaned from several images. The aim is to reduce uncertainties inherent in single-sensor data, and reduce the sheer amount of intelligence imagery data that can overwhelm intelligence analysts by developing tools to help analysts analyze intelligence imagery using Big Data.

U.S. intelligence experts are considering a project to blend data from satellite-based multispectral imaging sensors and visible-light sensors to detect heavy building projects and highway construction from space.

The U.S. Intelligence Advanced Projects Agency (IARPA) has launched its SMART project, which will rely on geographical information from satellite cameras, and develop multi–spectral and multi–temporal sensor processing to overlay data from infrared and multispectral sensors to make the intelligence analyst’s job easier.

According to, IARPA is seeking automated broad-area search, monitoring, and analysis of man-made activities based on data fusion.

While one sensor may have resolution sufficient to detect changes and man-made disturbances, intelligence experts still struggle with the inability to analyze images over time because of infrequent satellite orbits or weather cover.

IARPA experts want to push the technology state of the art in high-performance analytics that scales to extremely large data sets; data mining, ranking and visualization; and image analyst tools.

By blending data from several different electro-optical sensors, IARPA experts want to improve the ability to detect and monitor man-made disturbances to track the progress of major construction projects.