Urban modeling has increasingly gathered attention for urban resilience simulation studies. However, existing methodologies often fall short in reconstructing detailed building facades necessary for Level of Detail (LoD) 3 modeling. This study presents an automated LoD-3 reconstruction framework using oblique UAV images, which remains underexplored for efficient and scalable LoD-3 reconstruction. The proposed method integrates photogrammetry-based processing with deep learning-based window detection. Our approach consists of extracting roof structures, generating simulated facade images, detecting windows , and mesh intersections. Based on the photogrammetry, cameras are simulated to generate facade images of buildings. The YOLOv5-based window detection is followed using these simulated images. A ray-mesh intersection algorithm is implemented by projecting detected bounding boxes of windows onto the reconstructed LoD-2 model. The final LoD-3 model is exported in CityJSON format for seamless integration into urban simulation applications. Experimental results demonstrate that this approach shows the feasibility of an efficient and scalable solution for large-scale LoD-3 reconstructions.