Intersection-based Spatial Annotation of Trajectories with Linked Data

Abstract

Smart cities are characterized by providing new services through Information and Communications Technologies. However, it is important to gather data from citizens to discover new knowledge about certain aspects of a city. One example of a rich domain for collecting data in a smart city is exploring the use of mobile fitness applications. Users usually record outdoor activities in the form of trajectories, which can later be acquired for further analysis. In this work, we leverage Semantic Web technologies to propose an annotation algorithm that segments trajectories according to their spatial context. We demonstrate how the method works and the impact of OpenStreetMap related ontologies in the annotation process.