Accurate building footprints and 3D models are nowadays essential for a wide range of urban applications, yet the generation of Level of Detail 2 (LOD2) models remains constrained by the availability of dense 3D data such as LiDAR or image matching products. While these sources provide high geometric accuracy, they are costly to acquire and update, creating a gap between data availability and the increasing demand for city-scale 3D modelling. Recent advances in deep learning enable monocular height estimation from aerial imagery, offering a potential alternative to traditional 3D data sources. However, integrated workflows that combine image-based inference with structured 3D reconstruction are still limited. This paper presents CityZen, a point cloud-free workflow for LOD2 building reconstruction from only RGB orthophotos. The proposed approach integrates monocular height estimation (evaluating DSMNet, HTC-DC-Net and TSE-Net), roof type classification and model-driven reconstruction within a unified pipeline. Building footprints are used as geometric constraints, while inferred height and semantic cues guide the generation of consistent 3D structures. The proposed framework, available at <code>https://github.com/3DOM-FBK/citizen</code>, enables scalable and practical LOD2 city modelling using widely available aerial orthophotos, reducing dependency on costly 3D data acquisition.