Diploma and Master Theses (authored and supervised):

S. Podaras:
"Automated Classification of Road-Surface Types Based on Crowd-Sourced Data";
Supervisor: M. Wimmer; Institut für Computergraphik und Algorithmen, 2017; final examination: 2017-04-28.

English abstract:
This thesis presents a method to automatically estimate road-surface types based on crowd-sourced and open source data to give cyclists an overview of the road conditions along a cycle route.

Automatic classification of land-cover has been an active research field in recent years and mainly focuses on the classification of areas based on digital satellite and aerial imagery. Performing classification of road-surfaces based on such images bears some special challenges because roads have a width of only a few pixels on these photos, which makes it difficult to successfully apply classical image-analysis methods. Problems are caused by mixed pixels, which do not belong to a single surface class exclusively. Due to objects occluding the street, like for example trees and cars, it is difficult to isolate the streetīs actual surface from the rest of the image. This biases the classification procedure and may cause faulty results. Furthermore, aerial images of high spatial resolution are only available with a small range of spectral bands.

This thesis proposes an alternative approach for road-surface classification by utilizing open source data with a focus on data from the project OpenStreetMap (OSM). OSM is an online mapping project which collects geographical data and makes it available freely by providing a digital world map. Data is collected by users on a voluntary basis. OSM offers its users the possibility to add various properties to streets by making textual annotations. From these so-called tags it is possible to deduce road-surface properties for numerous roads by using methods from pattern recognition. The system is designed so it can be extended with additional data from other sources (e.g., height information) to improve classification results. Classification takes place at two levels, based on a coarse-to-fine-grained surface taxonomy.

The method was evaluated on different testing areas in Austria and Liechtenstein. At the coarse-grained level, up to 90% of streets were correctly classified. At the fine-grained level, up to 60% of streets were correctly classified. The advantage of the proposed method is that it is fast and applicable to regions worldwide at low cost, as long as sufficient OSM data for a certain region is available.

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.