Visualizing Routes with AI-Discovered Street-View Patterns
This work addresses the need for better visualization tools in map services for route planning by incorporating visual patterns, though it is incremental as it builds on existing AI and visualization techniques.
The paper tackled the problem of integrating street-level visual appearances into driving route planners by developing a system that uses AI to discover spatial imagery patterns from street-view images and incorporates them into an interactive visualization prototype called VivaRoutes, with a user study showing it helps users explore routes effectively.
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.