Running summarizing tools on a new article

Abstract: Urban drone delivery increasingly requires autonomous landing in courtyards, curbside bays and rooftop pads, where perception, communication and local micro-weather can change within a few seconds of descent. This paper presents Hiera-Seg, a hierarchical segmentation and edge-adaptive learning model for landing-risk assessment in unmanned aerial vehicle delivery. The model decomposes the landing scene into traversability, obstacle, surface-stability and exposure layers, and then fuses them through a calibrated risk head updated by lightweight edge clients. A controlled evaluation was constructed from 18,420 annotated image-depth frames, 612 descent sequences and five representative landing environments, including rooftop, sidewalk, courtyard, service-lane and campus-pad scenes. Against a non-adaptive DeepLab-style baseline, Hiera-Seg improved mean intersection over union from 78.6% to 84.9%, reduced expected calibration error from 0.116 to 0.052 and lowered high-risk false clearance from 9.8% to 4.1%. The edge adaptation module reached 27.4 ms median inference latency on an embedded GPU and retained 96.2% of centralized accuracy after communication was limited to 1.6 MB per update round. These results show that landing risk is estimated more reliably when scene hierarchy and edge-specific operating conditions are learned together rather than handled as separate perception and decision stages.