With data provided by modern meter-resolution synthetic aperture radar (SAR) sensors and advanced multipass interferometric techniques such as tomographic SAR inversion (TomoSAR), it is now possible to reconstruct the shape and monitor the undergoing motion of urban infrastructures on the scale of centimeters or even millimeters from space in very high level of details. The retrieval of rich information allows us to take a step further toward generation of 4-D (or even higher dimensional) dynamic city models, i.e., city models that can incorporate temporal (motion) behavior along with the 3-D information. Motivated by these opportunities, the authors proposed an approach that first attempts to reconstruct facades from this class of data. The approach works well for small areas containing only a couple of buildings. However, towards automatic reconstruction for the whole city area, a more robust and fully automatic approach is needed. In this paper, we present a complete extended approach for automatic (parametric) reconstruction of building facades from 4-D TomoSAR point cloud data and put particular focus on robust reconstruction of large areas. The proposed approach is illustrated and validated by examples using TomoSAR point clouds generated from a stack of TerraSAR-X high-resolution spotlight images from ascending orbit covering an approximately 2- $hbox{km}^{2}$ high-rise area in the city of Las Vegas.