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The integration of unmanned aerial vehicles (UAVs) into shared airspace forbeyond visual line of sight (BVLOS) operations presents significant challengesbut holds transformative potential for sectors like transportation,construction, energy and defense. A critical prerequisite for this integrationis equipping UAVs with enhanced situational awareness to ensure safeoperations. Current approaches mainly target single object detection orclassification, or simpler sensing outputs that offer limited perceptualunderstanding and lack the rapid end-to-end processing needed to convert sensordata into safety-critical insights. In contrast, our study leverages radartechnology for novel end-to-end semantic segmentation of aerial point clouds tosimultaneously identify multiple collision hazards. By adapting and optimizingthe PointNet architecture and integrating aerial domain insights, our frameworkdistinguishes five distinct classes: mobile drones (DJI M300 and DJI Mini) andairplanes (Ikarus C42), and static returns (ground and infrastructure) whichresults in enhanced situational awareness for UAVs. To our knowledge, this isthe first approach addressing simultaneous identification of multiple collisionthreats in an aerial setting, achieving a robust 94% accuracy. This workhighlights the potential of radar technology to advance situational awarenessin UAVs, facilitating safe and efficient BVLOS operations.

Segmentation of Drone Collision Hazards in Airborne RADAR Point Clouds Using PointNet