isprsannals II 3 W3 91 2013
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Our approach makes no assumption of the nature (sparse, dense) or origin (LIDAR, image matching) of the point clouds and further distinguishes, automatically, between different
The achieved results show the off-ground region (Figure 5b), the roof region exploiting the spectral information (Figure 5c) and the final classification (Figure
Buildings are usually characterized by vertical walls that define the footprint of each building and can be reconstructed using image matching on oblique
The original images with the input Stixels (left), the 2D mapping results overlaid on Google Earth images (center) and the 3D representation (right) using the OctoMap representation
Once the features of the basic pitched roof component are recognized (top left), the feature recognition will work on the grey (sub-surface) segment (top middle) with the
The authors thank the IGN (Institut National de l’Information Géographique et Forestière, France) for providing reliable ground truth data. Adaptive Filtering of
Our system works with more than 140 different classes of traffic signs and does not require labor-intensive labelling of a large amount of training data due to the
Figure 2: A sample scene from the Vaihingen data set: (a) raw LIDAR points overlaid on an orthoimage, (b) building mask and non-ground LIDAR points, (c) grouping and clustering of
