Visual inspections of aircraft exterior surface are usually required in aircraft maintenance routine. It becomes a trend to use mobile robots equipped with sensors to perform automatic inspections as a replacement of manual inspections which are time-consuming and error-prone. The sensed data such as images and point cloud can be used for further defect characterization leveraging the power of machine learning and data science. In such a robotic inspection procedure, a precise digital model of the aircraft is required for planning the inspection path, however, the original CAD model of the aircraft is often inaccessible to aircraft maintenance shops. Thus, sensors such as 3D Laser scanners and RGB-D (Red, Green, Blue, and Depth) cameras are used because of their capability of generating a 3D model of an interested object in an efficient manner. This paper presents a two-stage approach of automating aircraft scanning with a UAV (Unmanned Aerial Vehicle) equipped with an RGB-D camera for reconstructing a digital replica of the aircraft when its original CAD model is not available. In the first stage, the UAVcamera system follows a predefined path to quickly scan the aircraft and generate a coarse model of the aircraft. Then, a full-coverage scanning path is computed based on the coarse model of the aircraft. In the second stage, the UAV-Camera system follows the computed path to closely scan the aircraft for generating a dense and precise model of the aircraft. We solved the Coverage Path Planning (CPP) problem for the aircraft scanning using Monte Carlo Tree Search (MCTS) which is a reinforcement learning technique. We also implemented the Max-Min Ant System (MMAS) strategy, a population-based optimization algorithm, to solve the CPP problem and demonstrate the effectiveness of our approach.