Have you ever given a tank entity the command to follow a road and then thought you were simulating a "Dancing With The Stars" episode? Have you ever asked an Internet utility to provide a travel route and then found the result unintuitive and longer than expected? In each case, problems in the digital representation of the road networks can be to blame. The tank entity might actually be following a road that includes severe kinks and kickbacks. The route planner might be defeated by breaks in the road network.

Much of the digital data used to create simulation representations of the physical environment comes from the National Geospatial-Intelligence Agency (NGA). While the NGA has a large holding of internally-produced geospatial data, the agency's current strategy includes substantial data production under contract as well as a large cooperative effort with other nations under the Multinational Geospatial Co-production Program (MGCP). The development, codification, and enforcement of detailed quality standards has emerged as key to this acquisition strategy. The MGCP countries have jointly produced detailed requirements for the relationships between and quality characteristics of feature data elements; however, these specifications have been produced for human consumption. In some cases, the documentation lacks the specificity necessary to support algorithm development to enforce the standards.

This paper describes the type of quality standards that are to be applied in the future production of geo-spatial feature data and illustrates a process to transform semantic descriptions into specific guidance suitable for software implementation. The process includes experimentation to determine appropriate geometric reasoning strategies that will permit identification of substandard data while minimizing false positive notifications. The paper describes a typical problem, the experiment designed to address the problem, and the results of conducting the experiment. The paper concludes with observations on the potential impact of these geospatial data developments on the modeling and simulation community.