Automatic extraction of urban road networks from multi-view aerial imagery
In this paper, we present work on automatic road extraction from high-resolution aerial imagery taken over urban areas. In order to deal with the high complexity of this type of scenes, we integrate detailed knowledge about roads and their context using explicitly formulated scale-dependent models. The knowledge about how and when certain parts of the road and context model are optimally exploited is expressed by an extraction strategy. The key feature of the presented approach is the integral treatment of three essential issues of object extraction in complex scenes. (1) Specific parts of the road model and extraction strategy are automatically adapted to the respective contextual situation. (2) The extraction incorporates components for self-diagnosis that internally evaluate hypotheses indicating their relevance for further processing. (3) Multiple views on the scene are utilized in different ways. Redundancies in the extraction are exploited, occlusions are predicted and obviated, and a 3D object description is generated. The results achieved with our approach show that a stringent realization of these issues enables the extraction of roads even if their appearance is heavily affected by other objects. Based on an external evaluation of the results, we discuss advantages but also remaining deficiencies of this approach.
aerial imagery road extraction urban areas image understanding multi-view imagery road lanes road markings