Correlation-Aware Multi-UAV Detection Fusion for Probabilistic Emergency Mapping

Multi-UAV emergency monitoring can enlarge spatial coverage, but overlapping fields of view create dependent detections that violate the conditional-independence assumption of conventional Bayesian accumulation. This study presents a correlation-aware method that converts georeferenced neural-detector outputs from multiple unmanned aerial vehicles (UAVs) into a calibrated probabilistic emergency map. Detector likelihoods are empirically calibrated, projected detections are distributed over a spatial grid, and repeated observations are down-weighted through an effective sample size derived from viewpoint and temporal similarity. The method is evaluated with a geometrically exact camera simulation and a detector emulator calibrated from 10,463 real annotated frames. In a synthetic model with an exactly computable posterior, correlation-aware fusion reduces the median excess Brier score from 23.4% for naive fusion to 3.9%, and the worst case from 91.5% to 8.2%. In the flight simulation, expected calibration error decreases from 0.046 to 0.013 for tightly clustered viewpoints. A weighted Voronoi coordination layer additionally reduces coverage time by a factor of 3.47 and duplicated imaging from 100% to 10.0% over 1 km² with five UAVs. The results show that explicitly accounting for dependence is important for probabilistic multi-UAV situational awareness.