Street-level visual appearances play an important role in studying social systems, such as understanding the built environment, driving routes, and associated social and economic factors. It has not been integrated into a typical geographical visualization interface (e.g., map services) for planning driving routes. In this paper, we study this new visualization task with several new contributions. First, we experiment with a set of AI techniques and propose a solution of using semantic latent vectors for quantifying visual appearance features. Second, we calculate image similarities among a large set of street-view images and then discover spatial imagery patterns. Third, we integrate these discovered patterns into driving route planners with new visualization techniques. Finally, we present VivaRoutes, an interactive visualization prototype, to show how visualizations leveraged with these discovered patterns can help users effectively and interactively explore multiple routes. Furthermore, we conducted a user study to assess the usefulness and utility of VivaRoutes.

街道级视觉外观、地理可视化界面、语义潜在向量、空间图像模式和驾驶路径规划器是这篇论文的关键词。该论文研究了将街道级视觉外观整合到地理可视化界面（例如地图服务）以规划驾驶路径的新型可视化任务，并提出了多项新的贡献，并通过VivaRoutes交互式可视化原型展示了这些发现模式如何帮助用户有效和交互地探索多条路径，并通过用户研究评估了VivaRoutes的实用性和效用。

利用人工智能发现的街景模式可视化路线