Reliable and accurate methods for road network detection and classification in satellite imagery are essential to many applications. We present an image vectorization approach to the road network extraction from digital imagery that is based on proximity graph analysis. An input to the presented approach is spectrally segmented image that contains a set of candidate road fragments. First, non-intersecting contours are extracted around image elements. Second, constrained Delaunay triangulation and Chordal Axis transform are used to extract global centerline topology characterization of the delineated candidate road fragments. Then, constrained Delaunay triangulation of the extracted set of attributed center lines is performed. The tessellation grid of the Delaunay triangulation covers the set of candidate road fragments and is adapted to its structure, since triangle vertices and edges reflect the shapes and spatial adjacency of the segmented regions. The produced Delaunay network edges can be attributed with spectral and structural characteristics that are used for spatial analysis of the edges relations. This leads to the reconstruction of the road network out of the Delaunay edges. A subset of the tessellation grid contains the Euclidian Minimum Spanning Tree that provides an approximation of road network. The approach can be generalized to the multi-criteria MST and multi-criteria shortest path algorithms to integrate other factors important for road network extraction, in addition to proximity relations considered by standard MST.