Interchange Recognition Method Based on CNN

doi:10.11947/j.AGCS.2018.20170265

Abstract

Abstract: The identification and classification of interchange structures in OSM data can provide important information for the construction of multi-scale model, navigation and location services, congestion analysis, etc. The traditional method of interchange identification relies on the low-level characteristics of artificial design, and cannot distinguish the complex interchange structure with interference section effectively. In this paper, a new method based on convolutional neural network for identification of the interchange is proposed. The method combines vector data with raster image, and uses neural network to learn the fuzzy characteristics of the interchange, and classifies the complex interchange structure in OSM. Experiments show that this method has strong anti-interference, and has achieved good results in the classification of complex interchange shape, and there is room for further improvement with the expansion of the case base and the optimization of neural network model.

Key words: cartographic generalization, interchange recognition, convolutional neural network, grid vector combination, pattern recognition

CLC Number:

P208

HE Haiwei, QIAN Haizhong, XIE Limin, DUAN Peixiang. Interchange Recognition Method Based on CNN[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(3): 385-395.

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