Inspecting the exteriors of buildings is a slow and risky task for workers, especially in high-rise buildings. Moreover, some areas are difficult to reach for large buildings, and in some cases, the inspections cannot be adequately done. In recent years, there has been an increase in open-source artificial intelligence (AI) technologies and commercially available Unmanned Aerial Vehicles (UAVs) with AI-assisted deep learning capabilities. They can provide a low-cost, open-source, and customizable methodology for building exterior inspections that are readily accessible for construction and facility managers. This study presents a methodology to use UAVs and deep learning technology to conduct an automated inspection for cracks on high-rise buildings - improving the efficiency of the process and the workers' safety while reducing data-collection errors. The proposed methodology is divided into four components: 1) Developing a UAV system to capture the exterior wall images of the building in an autonomous way, 2) Collecting data, 3) Processing and analyzing the images captured for cracks using deep learning, and 4) Rendering the identified locations of the cracks on a 3D model of the building, constructed using photogrammetry, for clear visualization. This study focuses on the virtual simulation of the methodology. The UAV used contains a built-in camera to capture the images of the building from different sides. Data Collection, Image-Analysis, and Photogrammetry are done using publicly available open-source deep learning and simulation technologies. The generated code for the UAV simulation and the crack detection algorithm with the pre-trained data model are released on GitHub.