  In recent years, consumer Unmanned Aerial Vehicles have become very popular,
everyone can buy and fly a drone without previous experience, which raises
concern in regards to regulations and public safety. In this paper, we present
a novel approach towards enabling safe operation of such vehicles in urban
areas. Our method uses geodetically accurate dataset images with Geographical
Information System (GIS) data of road networks and buildings provided by Google
Maps, to compute a weighted A* shortest path from start to end locations of a
mission. Weights represent the potential risk of injuries for individuals in
all categories of land-use, i.e. flying over buildings is considered safer than
above roads. We enable safe UAV operation in regards to 1- land-use by
computing a static global path dependent on environmental structures, and 2-
avoiding flying over moving objects such as cars and pedestrians by dynamically
optimizing the path locally during the flight. As all input sources are first
geo-registered, pixels and GPS coordinates are equivalent, it therefore allows
us to generate an automated and user-friendly mission with GPS waypoints
readable by consumer drones' autopilots. We simulated 54 missions and show
significant improvement in maximizing UAV's standoff distance to moving objects
with a quantified safety parameter over 40 times better than the naive straight
line navigation.
