车辆轨迹数据的道路学习提取法

doi:10.11947/j.AGCS.2020.20190305

摘要/Abstract

摘要： 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，深度学习的快速发展为该问题的解决提供了一种思路与方法。本文针对车辆轨迹数据的车行道级道路提取问题，引入深度学习领域的生成式对抗网络，利用残差网络构建深层网络和多尺度感受野感知轨迹数据不同细节特征，构建了基于条件生成式对抗网络的轨迹方向约束下车行道级道路提取模型。首先提出了朝向-颜色映射栅格化转换方法，实现轨迹朝向信息向HSV颜色空间的转换；然后利用样本数据学习模型参数；最后将训练模型应用到郑州、成都、南京3个试验区域提取车行道级道路数据。试验结果表明，本文方法能够有效地提取完整的车行道级道路数据。

关键词: 深度学习, 条件生成式对抗网络, 车辆轨迹, 车行道级道路提取, 朝向-颜色映射

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

中图分类号:

P208

陆川伟, 孙群, 陈冰, 温伯威, 赵云鹏, 徐立. 车辆轨迹数据的道路学习提取法[J]. 测绘学报, 2020, 49(6): 692-702.

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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