Multi-Modal Contextualization of Trajectory Data for Advanced Analysis

Summary Rising amounts of generated geospatial data, either trajectory-like tracking data, raster-like imagery, or vector-like mappings as in OpenStreetMap (OSM), grow the need for multi-modal algorithmic analysis. Existing machine-learning-based algorithms contradictly mainly focus on image and textual input representations and cannot deal with other modes of geospatial data. Therefore, we propose a novel method to contextualize vector-like trajectory data with surrounding data to create easy-to-be-analyzed image-like representations. Our approach includes the proposition of a chase-cam-like scanline over space according to the trajectory’s speed and possibly smoothed orientation. Thereby, surrounding pixels in the vicinity of the trajectory points are accumulated along the scanline and are combined into a visual representation of the trajectory. To show the potential effects of our work, we predict traffic regulations for trajectory sections in the vehicle speed dataset based on our proposed trajectory-based sampling of orthophotos in the same region. This proposes a new way of using multi-modal data sources (trajectories and airborne imagery) to extract road metadata.