The demand for 3D maps of cities and road networks is steadily increasing and mobile mapping systems are often the preferred geo-data acquisition method for capturing such scenes. Manual processing of point clouds is labour intensive and thus time consuming and expensive. This article focuses on the state of the art of automatic classification and 3D mapping of road objects from point clouds acquired by mobile mapping systems and considers the feasibility of exploiting scene knowledge to increase the robustness of classification.
|Journal||GIM International: the worldwide magazine for geomatics|
|Publication status||Published - 2017|