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With improved sensor resolution and advanced multi-pass interferometric techniques such as SAR tomographic inversion(TomoSAR), it is now possible to reconstruct both shape and motion of urban infrastructures. These sophisticated techniques notonly opens up new possibilities to monitor and visualize the dynamics of urban infrastructure in very high level of details but alsoallows us to take a step further towards generation of 4D (space-time) or even higher dimensional dynamic city models that canpotentially incorporate temporal (motion) behaviour along with the 3D information. Motivated by these chances, this paper presentsa post processing approach that systematically allows automatic reconstruction of building façades from 4D point cloud generatedfrom tomographic SAR processing and put the particular focus on robust reconstruction of large areas. The approach is modular andconsists of extracting facade points via point density estimation procedure based on directional window approach. Segmentation offacades into individual segments is then carried out using an unsupervised clustering procedure combining both the density-basedclustering and the mean-shift algorithm. Subsequently, points of individual facade segments are identified as belonging to flat orcurved surface and general 1st and 2nd order polynomials are used to model the facade geometry. Finally, intersection points of theadjacent façades describing the vertex points are determined to complete the reconstruction process. The proposed approach isillustrated and validated by examples using TomoSAR point clouds over the city of Las Vegas generated from a stack of TerraSARXhigh resolution spotlight images

Robust Building Facade Reconstruction from Spaceborne TOMOSAR Points