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This master thesis focuses on practical application of Convolutional NeuralNetwork models on the task of road labeling with bike attractivity score. Westart with an abstraction of real world locations into nodes and scored edgesin partially annotated dataset. We enhance information available about eachedge with photographic data from Google Street View service and with additionalneighborhood information from Open Street Map database. We teach a model onthis enhanced dataset and experiment with ImageNet Large Scale VisualRecognition Competition. We try different dataset enhancing techniques as wellas various model architectures to improve road scoring. We also make use oftransfer learning to use features from a task with rich dataset of ImageNetinto our task with smaller number of images, to prevent model overfitting.

Estimating Bicycle Route Attractivity from Image Data