Acquiring and maintaining a situational awareness of a space require vigilant and robust systems – combining both local and remote sensing platforms. Furthermore, as new disruptive hardware and software challenge legacy systems there is a need to modernize those systems and implement new tools to streamline the workflows of users. Also, new data resources are needed to expand into new domains, environments or fuse various sensor platforms for better automation in low shot detection contexts. On the other hand, the exponential increase of satellite products posed a looming big data problem for analysts. These kind of Intelligence, Surveillance and Reconnaissance (ISR) applications in the security and defense domain typically involve computer vision algorithms and methods, but the evaluation of the data resources used to train and evaluate such tools are also important. There is a need to reduce the burden on providing site security and actionable intelligence from big data sources. Also, while fully automated kill chains are against current DOD policy, there is a need for tools to assist in the lining up of targets correctly. To this end, Lynntech has developed a number of ways to evaluate automated detection or classification systems against various challenges, posed by a particular dataset, environment, or adversarial entity. The ability to visualize how a tool “sees” a dataset is a particular need for transferring tools between domains. We also specialize in enhancing the realism of physics-based simulations of data in various domains to facilitate better “in real life” (IRL) performance of derivative tools. The overall goal is to reduce the data burden, and increase the robustness of systems by leveraging the best that legacy systems have to offer with newer unsupervised learning techniques.