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In recent years, consumer Unmanned Aerial Vehicles have become very popular,everyone can buy and fly a drone without previous experience, which raisesconcern in regards to regulations and public safety. In this paper, we presenta novel approach towards enabling safe operation of such vehicles in urbanareas. Our method uses geodetically accurate dataset images with GeographicalInformation System (GIS) data of road networks and buildings provided by GoogleMaps, to compute a weighted A* shortest path from start to end locations of amission. Weights represent the potential risk of injuries for individuals inall categories of land-use, i.e. flying over buildings is considered safer thanabove roads. We enable safe UAV operation in regards to 1- land-use bycomputing a static global path dependent on environmental structures, and 2-avoiding flying over moving objects such as cars and pedestrians by dynamicallyoptimizing the path locally during the flight. As all input sources are firstgeo-registered, pixels and GPS coordinates are equivalent, it therefore allowsus to generate an automated and user-friendly mission with GPS waypointsreadable by consumer drones' autopilots. We simulated 54 missions and showsignificant improvement in maximizing UAV's standoff distance to moving objectswith a quantified safety parameter over 40 times better than the naive straightline navigation.

Autonomous navigation for low-altitude UAVs in urban areas