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In this paper we propose an approach to multi-class semantic segmentation of urban areas in high-resolution aerial images with classspecificobject priors for buildings and roads. What makes model design challenging are highly heterogeneous object appearances andshapes that call for priors beyond standard smoothness or co-occurrence assumptions. The data term of our energy function consistsof a pixel-wise classifier that learns local co-occurrence patterns in urban environments. To specifically model the structure of roadsand buildings, we add high-level shape representations for both classes by sampling large sets of putative object candidates. Buildingsare represented by sets of compact polygons, while roads are modeled as a collection of long, narrow segments. To obtain the finalpixel-wise labeling, we use a CRF with higher-order potentials that balances the data term with the object candidates. We achieveoverall labeling accuracies of > 80%

SEMANTIC SEGMENTATION OF AERIAL IMAGES IN URBAN AREAS WITH CLASS-SPECIFIC HIGHER-ORDER CLIQUES