Automatic road extraction based on multi-scale, grouping, and context

An approach for the automatic extraction of roads from digital aerial image ry is proposed. It makes use of several versions of the same aerial image w ith 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 loca l level. The automatic segmentation of the aerial image into different glob al contexts, i.e., rural, forest, and urban area, is used to focus the extr action to the most promising regions. For the actual extraction of the road s, edges are extracted in the original high resolution image (0.2 to 0.5 m) and lines are extracted in an image of reduced resolution. Using both reso lution levels and explicit knowledge about roads, hypotheses for road segme nts are generated. They are grouped iteratively into larger segments. in ad dition to the grouping algorithms, knowledge about the local context, e.g., shadows cast by a tree onto a road segment, is used to bridge gaps. To con struct the road network, finally intersections are extracted. Examples and results of an evaluation based on manually plotted reference data are given , indicating the potential of the approach.