The operation of the rapidly growing unmanned aerial vehicles (UAV) and the promising urban aerial mobility (UAM) could have a significant noise impact on the environment. In this work, we developed a cloud-based noise simulator to efficiently assess the environmental impact of UAM and UAV. The noise sources and long-distance propagation are computed by the propeller noise prediction models and an advanced Gaussian beam tracing method, respectively, in local high-performance computers. Users can define the working conditions and vehicle layer through a platform with a user-friendly graphical interface. In addition, the noise level distribution at the observers of interest such as the buildings can be visualized. By employing advanced interpolation methods or autonomous learning algorithms, the computations are efficiently accelerated such that the noise distributions are simultaneously displayed during flights of the vehicles. To better measure the noise impact on human perception, various noise metrics will be output for further analysis. By conducting the virtual flights using the simulator, the noise impact in each flight state and atmospheric condition of different vehicles can be predicted, which will then facilitate the low-noise flights for both UAV and UAM.