On the evaluation of higher-order cliques for road network extraction

The automatic extraction of road networks is an interesting and challenging task. In spite of significant research efforts this problem remains largely open. In our work we attempt to leverage context at two different levels to extract accurate and topologically correct road networks. Local context, in the form of powerful features extracted from large neighborhoods, exploits the layout of road pixels and their co-occurrence with visual patterns along the roads. Global context enforces the connectivity of roads in a network, by grouping individual pixels into longer road segments, modeled as large higher-order cliques in a Conditional Random Field. Here, we evaluate different ways of defining these cliques. It turns out that, with modern probabilistic inference techniques, using a smaller number of very large cliques is more efficient than splitting them into a larger number of shorter segments.