  3D city models can be generated from aerial images. However, the calculated
DSMs suffer from noise, artefacts, and data holes that have to be manually
cleaned up in a time-consuming process. This work presents an approach that
automatically refines such DSMs. The key idea is to teach a neural network the
characteristics of urban area from reference data. In order to achieve this
goal, a loss function consisting of an L1 norm and a feature loss is proposed.
These features are constructed using a pre-trained image classification
network. To learn to update the height maps, the network architecture is set up
based on the concept of deep residual learning and an encoder-decoder
structure. The results show that this combination is highly effective in
preserving the relevant geometric structures while removing the undesired
artefacts and noise.
