LARGE SCALE ROAD NETWORK EXTRACTION IN FORESTED MOUTAINOUS AREAS USING AIRBORNE LASER SCANNING DATA

In this work, we present an approach that is able to deal with large-scale road network mapping. While former methods focus on delineating patches of roads without computing a coherent road network, we formulate a very large number of road hypothesis that are pruned using a graph reasoning and weak a priori knowledge on road behavior. The initial solution is computed by means of two machine learning and pattern recognition state-of-the-art methods (namely, Random Forest classification and Marked Point Process) that allow to process very large areas in little time with very satisfactory results.