Road Extraction in Urban Areas Supported by Context Objects

In this contribution, we introduce our concept for automatic road extraction in urban areas using high resolution aerial imagery and dense height information. For facilitating the extraction we use a hierachical road model comprising different road elements (markings, lanes, junctions, road network) at different scales as well as context objects of roads like buildings and vehicles. Global contextual knowledge about roads in residential areas helps us to focus on certain image parts, and thus, to cope with the inherently high complexity of urban scenes. Road extraction is then performed in a mainly data driven fashion starting with the extraction of the basic road elements at the lowest level of the road model and reaching the top level, i.e., a topologically consistent road network, with the final steps of processing. On each step in between, hypotheses for the respective road elements are generated and validated, and thus, evidence about roads is collected and transfered to the next step. Since urban roads are often characterized by rather dense traffic the detection of vehicles and vehicle convoys plays a major role. The results of the currently implemented modules give us rise to further realize this concept.