This paper deals with automatic road extraction from SAR imagery. In general, automatically extracted road networks are not complete, i.e., gaps remain in the erxtracted network. Especially in SAR imagery many objects occlude road sections and cause gaps, due to the side looking geometry of the SAR sensor. In this paper an approach for automatic road extraction is proposed that is optimized for...

An approach for the automatic extraction of roads from digital aerial imagery is proposed. It is optimized for rural areas and makes use of versions of an aerial image with different resolutions. Roads are modeled as a network of intersections and links between these intersections, and are found by a grouping process. The context of roads is hierarchically structured into a global and a local l...

Many rural roads lack sharp, smoothly curving edges and a homogeneous surface appearance, hampering traditional vision-based road-following methods. However, they often have strong texture cues parallel to the road direction in the form of ruts and tracks left by other vehicles. This paper describes an unsupervised algorithm for following ill-structured roads in which dominant texture orientati...

Young drivers, especially males, have relatively more accidents than other drivers. Young driver accidents also have somewhat different characteristics to those of other drivers; they include single vehicle accidents involving loss of control; excess speed for conditions; accidents during darkness; accidents on single carriageway rural roads; and accidents while making cross-flow turns (i.e. tu...

In this paper we report on research on road extraction at TUM (Technische Universität München). We propose a scheme for road extraction in rural areas that integrates three different modules with specific strengths. The first module employs local grouping and uses multiple scales and context to reliably extract most parts of the road network. In order to connect these parts, the second module e...