Image-based Building Classification and 3d Modeling with Super-pixels

Due to an increasing amount of aerial data there is significant demand in automatic large-scale modeling of buildings. This work presents an image-driven method for automatic building extraction and 3D modeling from large-scale aerial imagery. We introduce a fast unsupervised segmentation technique based on super-pixels. Considering the super-pixels as smallest units in the image space, these regions offer important spatial support for an information fusion step and enable a generic modeling of arbitrary building footprints and rooftop shapes. In our three-staged approach we integrate both appearance information and height data to accurately classify building pixels and to model complex rooftops. We apply our approach to datasets, consisting many overlapping aerial images, with challenging characteristics. The classification pipeline is evaluated on ground truth data in terms of correctly labeled pixels. We use the building classification together with color and height for large-scale modeling of buildings.