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Learning the flight model for an Unmanned Aerial Vehicle (UAV) involves estimating stability and control parameters from flight data. A non-parametric approach to perform this task is to use Dependent Gaussian Processes (DGPs). It has many benefits, including not having to know a prior model structure, captures any dependencies embodied in the outputs and learns the system noise. However, the main drawback of this approach is the heavy computational cost which makes it prohibitive to learn the model from large test flight data sets. In addition, DGPs do not capture any non-stationary behavior in the aerodynamic coefficients. This paper presents a novel approach to address these issues while maintaining all the other benifits that was gained using DGPs. The proposed algorithm uses an additive sparse model that combines global and local Gaussian processes to learn a multi-output system. We demonstrate that having a combined approximation makes the model suitable for all regions of the flight envelope. To capture the global properties we introduce a new sampling method to gather information about the output correlations. Local properties were captured using a non-stationary covariance function with KD-trees for neighbourhood selection. This makes the model scalable to learn from high dimensional large-scale data sets. Finally, the method explained in this paper was demonstrated in several examples using real flight tests from a delta-winged UAV.