Collision probability estimation represents a crucial part of aviation safety analysis, providing the quantitative foundation for collision risk assessment focusing on unmanned aircraft vehicles (UAV) operating in rather dense urban airspace. This paper presents a related deterministic, wind-aware model that incorporates varying look ahead times. We model UAV position uncertainty as a Gaussian process that propagates following analytically computed covariance, impacted by a central disturbing factor: the wind, enabling physically grounded uncertainty estimation. Collision probability is computed using Gaussian probability density functions, integrated over representative geometric zones - cylindrical for UAV–UAV and cuboid for UAV–obstacle proximities. Applied to typical takeoff and landing procedures, the model shows strong flight phase dependence with UAV-obstacle configurations. Sensitivity analyses on wind uncertainty mapping, prediction horizon, and correlation assumptions demonstrate numerical stability, temporal consistency, and physically interpretable behavior under perturbations. The approach offers a computationally efficient foundation for collision probability under wind uncertainty that supports safety and efficient urban air mobility implementation.