Small Unmanned Aircraft Systems (sUAS) are expected to proliferate in low-altitude airspace and will require flight near buildings and over people. Robust urgent landing capabilities including landing site selection are critical for safety. However, conventional fixed-wing emergency landing sites such as open fields and empty roadways are rare in and around cities. Our work uniquely considers a city’s many unoccupied flat rooftops as possible nearby landing sites. We propose novel methods to identify flat rooftop buildings, isolate their flat surfaces, and find touchdown points that maximize distance to obstacles. We model flat rooftop surfaces as polygons that capture their boundaries and obstructions. We process satellite images, airborne LiDAR point clouds, and map building outlines to generate rooftop maps with a multi-stage machine learning pipeline. We propose a computational geometry method (Polylidar3D) that reliably extracts flat rooftop surfaces from archived data sources. We model risk as an innovative combination of landing site and path risk metrics and conduct a multi-objective Pareto front analysis for sUAS urgent landing in cities. A high-fidelity simulated city is constructed in the Unreal game engine with a statistically-accurate representation of rooftop obstacles. Fusion of Polylidar3D and RGBD semantic segmentation output shows improved intersection-over-union (IOU) accuracy in landing site identification compared to using LiDAR data only.


Time: September 22nd 5:30-6:30 pm

Location: WebEx