systems architecture or set of operating procedures, and so should be termed “disruptive.” Disruptive methods necessitate retraining and redesign at the least. However, it is likely that many of the tools will be introduced incrementally; therefore the transformation itself may feel evolutionary to those involved. Many of the problems involve extensions to spatial database management systems (S-DBMS), which have long been seen as different from the standard DBMS used in information technology and commerce. Such systems are essential to manage vast data holdings, yet only recently have they been adapted for geospatial data and the special needs of GEOINT.
Based on the committee’s knowledge of the hard problems in geographic information science (GIScience) and information from NGA (as described in earlier chapters) on the current and future challenges in developing GEOINT, the subset of hard geospatial research problems most relevant to NGA was selected as the list of “hard problems” identified here. Aspects that can be addressed in the short term versus the long term are discussed after each hard problem. Then, based on knowledge of current research and literature, and after considerable debate and discussion, the committee selected methods and techniques that seem most promising for addressing the hard problems. These are not ranked in any way, but were seen by the committee as potential starting points for future research. As a final step, a prioritization of the hard problems is proposed in Chapter 6.
In the post-9/11 world, persistent tracking, processing, exploitation, and dissemination of geospatial intelligence over geographic space and time is crucial. However, current sensor networks (i.e., remote sensing using satellites and aircraft) and database management systems are inadequate to achieve persistent TPED for many reasons. First, current sensor networks were designed for tracking fixed targets (e.g., buildings, military equipment). They are sparse in space and time, and it takes a long time (e.g., hours) to move sensors to focus on the desired geographic area of interest for the relevant time interval. Lastly, even if an appropriate network were employed, current databases do not scale up to the significantly higher data rates and volumes of data generated by deployed sensor arrays. Basic and applied research on next-generation sensors, sensor networks, and spatiotemporal databases is crucial to achieving persistent TPED. Of particular importance has been the rapid development and deployment of unpiloted aircraft with multiple sensor systems that