A Road Extraction Method Based on Conditional Generative Adversarial Nets

  Objectives  Road information extraction based on vehicle trajectory data is one of the hotspots and difficulties in the field of geographic information data acquisition. Traditional methods are faced with the problems of high accuracy of trajectory data source, complex road extraction algorithm model, and poor adaptability of different road extraction model parameters. In order to solve the above problems, a trajectory-road conversion model based on conditional generative adversarial nets is proposed, and we called it trajectory-to-road translation with conditional generative adversarial nets(TR-CGAN).

  Methods  Firstly, the trajectory data and the corresponding road data are raster processed in the sample area to construct the trajectory-road sample image pairs, then the parameters of TR-CGAN are learned based on the sample data as the prior knowledge. Through the continuous iteration of the one player game, the optimal generation result is gradually approached. Before that, according to the characteristics of vehicle trajectory data, this paper uses the control variable method and enumeration method to analyze the parameters of U-Net generator depth, discriminator receptive field size and objective function in the conversion model, so as to obtain the optimal structure of TR-CGAN.

  Results  Using the taxi track data in the third ring road of Zhengzhou city, the experiment results show that this proposed method can find new roads more effectively. At the same time, the trained TR-CGAN is compared with the raster road extraction method, and it is found that our method has stronger adaptability of trajectory data in both the sparse and dense areas of the trajectory, and the accuracy of generated road is higher.

  Conclusions  Our proposed method can realize road extraction based on trajectory data, and has better data adaptability and accuracy. In the further research, we can increase the type of sample data, so that the road extraction model can learn to generate more road types, such as ring road types. The model can be further optimized to extract two lane roads or even lane roads.