Urban Road Net Extraction Integrating Internal Evaluation Models

This paper focuses on internal quality measures for automatic road extraction from aerial images taken over urban areas. The motivation of this work is twofold: Firstly, any automatic system should provide the user with a small number of values indicating the reliability of the obtained results. This is often referred to as ”self-diagnosis” and is in particular a crucial part of automatic image understanding systems. Secondly, and more important in the scope of our research, a system designed for the extraction of man-made objects in complex environments (like roads in urban areas) inherently implies many decisions during the extraction process. Such decisions are highly facilitated when both low level features and high level objects are attached with confidence values indicating their relevance for further processing. The basic idea for defining evaluation criteria from which the confidence values can be calculated is to split the components of a semantic object model into two different types. The model components of the first type are used for extracting features, i.e., parts of the object, and the components of the other type serve as criteria for evaluating the quality of the extracted features. For guaranteeing an unbiased evaluation one has to ensure that model components belonging to different types are independent from each other (at least theoretically). We illustrate this concept by our system for road extraction in urban areas. Examples are given for both low level features like lines and ribbons as well as higher level features like lanes and road segments.