  This master thesis focuses on practical application of Convolutional Neural
Network models on the task of road labeling with bike attractivity score. We
start with an abstraction of real world locations into nodes and scored edges
in partially annotated dataset. We enhance information available about each
edge with photographic data from Google Street View service and with additional
neighborhood information from Open Street Map database. We teach a model on
this enhanced dataset and experiment with ImageNet Large Scale Visual
Recognition Competition. We try different dataset enhancing techniques as well
as various model architectures to improve road scoring. We also make use of
transfer learning to use features from a task with rich dataset of ImageNet
into our task with smaller number of images, to prevent model overfitting.
