Accurate tracking in urban environments necessitates target birth, survival, and detection models that quantify the impact of terrain and building geometry on the sequential estimation procedure. Current efforts assume that target trajectories are limited to fixed paths, such as road networks. In these settings a single airborne platform with a downward-facing camera is capable of fully observing a target, outside of a few obstructed regions that can be determined a priori (e.g. tunnels). However, many practical target types are not necessarily restricted to road networks and thus require knowledge of azimuthal shadowed regions to the sensor. In this paper, we propose the integration of geospatial data for an urban environment into a particle filter realization of a random finite set target tracking algorithm. Specifically, we use 3D building polygons to compute the azimuthal shadowed regions with respect to deployed sensor location. The particle filter predict and update steps are modified such that (1) target births are assumed to occur in line-of-sight (LOS) regions, (2) targets do not move into obstructions, (3) true target detections only occur in LOS regions. The localization error performance improvement for a single target Bernoulli filter under these modifications is presented using freely available building vector data of New York City.