一种基于条件生成式对抗网络的道路提取方法

A Road Extraction Method Based on Conditional Generative Adversarial Nets

基于车辆轨迹数据的道路信息提取是地理信息领域的热点也是难点之一,传统方法面临着轨迹数据源要求高、道路提取算法复杂、不同道路提取模型参数适应性不强等问题。针对以上问题,提出基于条件生成式对抗网络的轨迹地图向道路地图转换的轨迹-道路转换方法。该方法以轨迹数据与道路数据的对应关系为先验知识,通过"生成-博弈"的不断循环逐渐逼近最优结果,学习优化条件生成式对抗网络模型参数。首先将轨迹数据栅格化处理,然后基于样本数据学习优化条件生成式对抗网络参数,最后将训练好的模型应用到整个实验区域提取道路数据,发现所提方法可以有效地发现新增道路;同时将训练好的轨迹-道路转换模型与栅格化道路提取方法对比,发现所提方法在轨迹稀疏稠密区域都有更强的轨迹数据适应性,且生成的道路精确率更高。

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 trajectoryroad 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.