Small Unmanned Aircraft Systems (UAS) have diverse commercial applications. Risk mitigation techniques must be developed to minimize the probability of harm to persons and property in the vicinity of the aircraft. This paper presents an emergency flight planner combining sensor-based and map-based elements to collectively plan a landing path for a UAS that experiences an unexpected low energy condition while flying over a populated area. Focus is placed in this work on the use of public databases of population distribution, structure locations, and terrain to create an efficient-to-access cost map of the data. Safe landing plans are generated with an A* search algorithm shown to be feasible for real-time use with the cost map. Simulation-based case studies are presented of a quadrotor UAS operating within New York City to illustrate how different cost terms impact optimal path characteristics.