  Gravitational mass flows, such as avalanches, debris flows and rockfalls are
common events in alpine regions with high impact on transport routes. Within
the last few decades, hazard zone maps have been developed to systematically
approach this threat. These maps mark vulnerable zones in habitable areas to
allow effective planning of hazard mitigation measures and development of
settlements. Hazard zone maps have shown to be an effective tool to reduce
fatalities during extreme events. They are created in a complex process, based
on experience, empirical models, physical simulations and historical data. The
generation of such maps is therefore expensive and limited to crucially
important regions, e.g. permanently inhabited areas.
  In this work we interpret the task of hazard zone mapping as a classification
problem. Every point in a specific area has to be classified according to its
vulnerability. On a regional scale this leads to a segmentation problem, where
the total area has to be divided in the respective hazard zones. The recent
developments in artificial intelligence, namely convolutional neuronal
networks, have led to major improvement in a very similar task, image
classification and semantic segmentation, i.e. computer vision. We use a
convolutional neuronal network to identify terrain formations with the
potential for catastrophic snow avalanches and label points in their reach as
vulnerable. Repeating this procedure for all points allows us to generate an
artificial hazard zone map. We demonstrate that the approach is feasible and
promising based on the hazard zone map of the Tirolean Oberland. However, more
training data and further improvement of the method is required before such
techniques can be applied reliably.
