  High resolution Digital Elevation models, such as the (Big) grid terrain
model of Denmark with more than 200 billion measurements, is a basic
requirement for water flow modelling and flood risk analysis. However, a large
number of modifications often need to be made to even very accurate terrain
models, such as the Danish model, before they can be used in realistic flow
modeling. These modifications include removal of bridges, which otherwise will
act as dams in flow modeling, and inclusion of culverts that transport water
underneath roads. In fact, the danish model is accompanied by a detailed set of
hydrological corrections for the digital elevation model. However, producing
these hydrological corrections is a very slow an expensive process, since it is
to a large extent done manually and often with local input. This also means
that corrections can be of varying quality. In this paper we propose a new
algorithmic apporach based on machine learning and convolutional neural
networks for automatically detecting hydrological corrections for such large
terrain data. Our model is able to detect most hydrological corrections known
for the danish model and quite a few more that should have been included in the
original list.
