Invariant Spatial Relation-Based Road Network Graphics Retrieval for GPS Art

In recent years, people have increasingly sought to generate exercise trajectories that embody specific semantic shapes in order to create GPS art and share it on social platforms. This trend has created an urgent demand for navigation paths with specific semantic meanings on smartwatches and smartphones. Current methods mainly rely on manual design and lack efficient automation. Therefore, this study proposes a novel method for automatically obtaining navigation paths with specified shapes by retrieving graphics similar to the input graphic shape from the road network. This method uses invariant spatial relationships, such as turning angles and length ratios, along with graph matching techniques to establish one-to-one or one-to-many correspondences between line segments in the input individual graphics and those in the road network. This enables the retrieval of individual graphics within the road network. Based on this, a greedy strategy-based algorithm is proposed to solve the combined graphics retrieval problem. The results are evaluated to ensure high quality. The accuracy and effectiveness of our method are validated through experimental results using simulated and real road network data from five different regions. Furthermore, shape-constrained graphics retrieval expands the application domain of spatial scene matching.