However, it is also useful to put the problems in the context of the actual process of doing GEOINT, to better show how research will support the evolution of the various steps in the GEOINT process.
The next section puts forward a framework that describes the GEOINT2 process and information flow and then correlates the hard problems identified in Chapter 4 with the steps in the framework. Whereas the top 10 challenges are focused on the overall process and its outcomes, the framework described below is focused more on the individual steps of the process. Looking at the hard problems in both ways will allow the development of a more organized and robust research agenda and clarify the needed prioritization. These priorities are covered later in this chapter.
The key stages in geospatial information handling are to acquire, identify, integrate, analyze, disseminate, and preserve. In prior eras, these were separate and quite distinct tasks, even compartmentalized in terms of security. Early era space surveillance, for example, included segments of this cycle that took place in different states, at different times, and with different skills and affiliations such that most participants had no concept of the remainder of the cycle or were even aware that there was a cycle. This is the environment in which today’s GEOINT evolved, yet the NGA vision recognizes that the Cold War compartmentalized model is no longer adequate. A cycle that once could take months must now happen in minutes. There is no longer time to rely on fortuitous knowledge synthesis, nor can the system depend on specialists who spend their entire careers on a single problem.
The GEOINT2 analysis framework as envisioned by this committee is shown in Figure 6.1. The framework operates within, and is supported by, the existing cyberinfrastructure to sustain on-demand intelligence, to monitor and minimize uncertainties, and to preserve semantics in data and in GEOINT. GEOINT is a circular flow from newly acquired data to archived result. Yet thinking of the framework as a processing cycle with a clear beginning and end is a fallacy. In reality, new data arrive in a never-ending stream from instruments and the Internet. Thus, data input is a network of networks, remote sensing systems, cyberinfrastructure, sensor webs, additional intelligences (INTs), and so forth. Flowing out of the cycle is knowledge, in the form of specific decisions, reports, and actions, but also flowing back from this knowledge are new data. Both preservation and dissemination are outputs to specific communities, the “customers” for intelligence, but they are also sources of new data for future use. In GEOINT2, it should be as easy to acquire existing data with embedded links to the current time period as it is to acquire new data or