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The rapid pace of new commercial satellite constellation launches has led to a significant increase in the amount and availability of geospatial imagery. Unfortunately, no straightforward way currently exists for analysts to access and analyze all of that imagery. The current ad hoc, time-intensive approach requires gathering and curating data from a large number of available sources, downloading it to specific locations, and running it through separate suites of analytics tools.
The Geospatial Cloud Analytics (GCA) program is developing technology to rapidly access the most up-to-date commercial and open-source satellite imagery, as well as automated machine learning tools to analyze this data. Current approaches to geospatial analysis are ad hoc and time intensive, as they require gathering and curating data from a large number of available sources, downloading the data to specific locations, and running it through separate suites of analytics tools.
Mr. Chris Simi joined DARPA as a program manager in the Strategic Technology
Office in September 2018. Prior to DARPA, Simi spent 15 years in the
research office of the National Geospatial Intelligence Agency (NGA). At
NGA, Mr. Simi served as the senior scientist for remote sensing, developing
a large variety of advanced electro-optic systems and concepts.