Multi‐Domain Virtual Trajectory Classification Based on Spatiotemporal and Semantic Fusion

ABSTRACT Public map service platforms (PMSPs) aggregate spatial data, offering geographic information services across various domains such as health, environment, ocean, and agriculture. Spatial interactions between users and PMSPs give rise to virtual trajectories. Accurate multi‐domain virtual trajectory classification is crucial for establishing user profiles and enabling personalized recommendations. However, virtual trajectories derived from access logs exhibit temporal noise, characterized by localized layer sequence fluctuations due to misordered trajectory points, which degrades data quality. We propose the local dynamic sorting algorithm to ensure layer sequence smoothness through local trajectory point reordering. Furthermore, current research often neglects temporal features, specifically zooming and panning operational sequences during transitions, focusing primarily on spatial and semantic features of browsing targets, thereby limiting classification accuracy. We present a simplified representation of virtual trajectories as sequences of <browsing target, operation>, where browsing targets facilitate the extraction of spatial and semantic features, while operations capture temporal features. We develop an operation representation model, a color‐template method, and a POI spatial co‐occurrence model to extract these features, subsequently integrated into a temporal classification model. Evaluation using real‐world data from Tianditu demonstrates a 15.02% improvement in virtual trajectory classification accuracy compared to the benchmark. This study contributes to the precise delineation of PMSP users, fostering the development of personalized, intelligent geographic information services.