Up-to-date maps of a city’s urban structure types (USTs) are very important for effective planning, as well as for different studies and applications. We present an approach for the classification of USTs at the level of urban blocks based on high-resolution spaceborne radar imagery. Images obtained at the satellite’s ascending and descending orbits were used for extracting line and polygon features from each block. Most of the attributes considered in the classification concern the geometry of these features, as well as their spatial disposition inside the blocks. Furthermore, assuming the UST classes of neighboring blocks are probabilistically dependent, we explored the framework of probabilistic graphical models and propose different context-based classification models. These models differ with respect to (i) their type, i.e., Markov or conditional random fields, (ii) their parameterization and (iii) the criterion applied for establishing pairwise neighboring relationships between blocks. In our experiments, 1,695 blocks from the city of Munich (Germany) and five representative UST classes were considered. A standard classification performed with the Random Forest algorithm achieved an overall accuracy of nearly 70%. All context-based classifications achieved overall accuracies up to 7% higher than that. The results indicate that denser pairwise block-neighborhood structures lead to better results and that the accuracy improvement is higher when the strength of the contextual influences is proportional to the similarity of the neighboring blocks attributes.
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