isprs annals III 3 271 2016
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Note that model averaging in the case of end-to-end trained deep networks is in some sense a “stronger” ensemble than if one av- erages conventional classifiers such as decision
The experimental results on the synthetic image data: (a) the original image with red border lines of clipped images with a 20% overlap between two adjacent ones; (b) the
We represent facade objects by a simplified rectangular object model and present an energy model, which evaluates the agreement of a proposed configuration with the given image and
Domain adaptation techniques in transfer learning try to reduce the amount of training data required for classification by adapting a classifier trained on samples from a source
Given the generic model of a building in a boundary representa- tion, we recognize geometric relations such as orthogonality or parallelism automatically by hypothesis testing
We evaluate our classification method both on benchmark data from a mobile mapping platform and on a variety of large, terrestrial laser scans with greatly varying point density..
In method OS (Osendorfer et al., 2013), a descriptor learning architecture based on a Siamese CNN similar to our work was used, but the authors concentrated more on
Due to narrow streets in urban areas, ac- quiring the data from a terrestrial platform requires relative ori- entation, matching with the 3D building models using a gener- ated
