Road networks are the core infrastructure for connected and autonomous vehicles, but creating meaningful representations for machine learning applications is a challenging task. In this work, we propose to integrate remote sensing vision data into road network data for improved embeddings with graph neural networks. We present a segmentation of road edges based on spatio-temporal road and traffic characteristics, which allows to enrich the attribute set of road networks with visual features of satellite imagery and digital surface models. We show that both, the segmentation and the integration of vision data can increase performance on a road type classification task, and we achieve state-of-the-art performance on the OSM+DiDi Chuxing dataset on Chengdu, China.
翻译:公路网络是连接和自主车辆的核心基础设施,但为机器学习应用创造有意义的代表是一项艰巨的任务。在这项工作中,我们提议将遥感远景数据纳入公路网络数据,以改进与图形神经网络的嵌入。我们展示了基于时空公路和交通特点的公路边缘分割,从而丰富了具有卫星图像和数字地表模型视觉特征的公路网络属性组。我们表明,分割和整合远景数据可以提高道路类型分类任务的业绩,我们实现了中国成都OSM+Didi Chuxing数据集的最新业绩。