Road learning extraction method based on vehicle trajectory data

doi:10.11947/j.AGCS.2020.20190305

Abstract

Abstract: Road information extraction based on vehicle trajectory data is one of the hotspots and difficulties in the field of geographic information. The rapid development of depth learning provides a new idea and method for solving this problem. Aiming at the problem of roadway-level road extraction based on vehicle trajectory data, this paper introduces the generative adversarial nets in the field of deep learning, uses residual network to construct deep network and multi-scale receptive field to perceive different details of trajectory data, and constructs roadway-level road extraction model under the constraint of trajectory direction based on conditional generative adversarial nets. Firstly, the orientation-color mapping rasterization conversion method is proposed to transform the trajectory orientation information into HSV color space. Then, the parameters of the model are learned with the sample data. Finally, the trained model is applied to three experimental areas of Zhengzhou, Chengdu and Nanjing to extract the road data at the roadway level. The experimental results showed that the proposed method can effectively extract the complete road data at the roadway level.

Key words: deep learning, conditional generative adversarial nets, vehicle trajectory, roadway-level road extraction, orientation-color mapping

CLC Number:

P208

LU Chuanwei, SUN Qun, CHEN Bing, WEN Bowei, ZHAO Yunpeng, XU Li. Road learning extraction method based on vehicle trajectory data[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(6): 692-702.

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