Location and time are critical to the success of many organizations’ missions. Sensors, software, processors, vehicles, and human analysts work together to accomplish these tasks of detecting and identifying specific entities as quickly as possible for these missions. This work aims to make a contribution by providing a team-based detection and identification performance model incorporating the theory of Distributed Situational Awareness (DSA) and its effect on completing a specific task. The task being the ability to detect and identify a specific entity within a complex urban environment. Conditions to accomplish the task is the utilization of two unmanned aerial vehicles mounted with electrooptical sensors, operated by two analysts, creating a team to execute this task. Our results provide an additional resource on the how technology and training might be utilized to find the best performance given these certain conditions and missions. A highly trained team might improve their performance with this technology, or a team with low training could perform at a high level given the appropriate technology in limited time scenarios. More importantly, the model presented in this paper provides an evaluation tool to compare new technologies and their impact on teams. Specifically, it enables answering questions, such as: is an investment in new technology appropriate if investing in additional training produces the same performance results? Future performance can also be evaluated based on the team’s level of training and use of technology for these specific tasks.