A large number of automatic or semi-automatic road extraction methods have sprung up,but the generated products usually lacked navigation attribute information,such as road hierarchies,road speed limits,etc.,which restrict intelligent nav-igation services such as"main road priority"and"speed limit alert".Therefore,taking road sections as the unit of analysis and considering the strong correlation between adjacent upstream and downstream road sections,a method for mining attribute in-formation of road hierarchies and road speed limits was proposed.First,we preprocessed tracks and road networks and realized the connection between track points and road sections.Then,multi-modal features were designed based on the understanding of the data and the task.Finally,the random forest was used to recognize road hierarchies and road speed limits,taking into account the information of the target road segments and their upstream and downstream adjacency information.Compared with single-class features,the integration features of road networks and trajectories improve the classification accuracy of road hier-archies and road speed limits;compared with the classification of road hierarchies and road speed limits considering only the target road segment,the classification of road hierarchies and road speed limits considering spatial adjacency information is more effective.
维普
万方数据