COLTRANE: ConvolutiOnaL TRAjectory NEtwork for Deep Map Inference
This addresses the challenge of map inference for unusual geospatial sites like airports and pedestrian paths, where existing methods often fail, representing a domain-specific advancement.
The paper tackles the problem of automatic road map generation from GPS trajectories across diverse environments, achieving up to 37% improvement in F1 scores over existing methods on city roads and airport tarmac datasets.
The process of automatic generation of a road map from GPS trajectories, called map inference, remains a challenging task to perform on a geospatial data from a variety of domains as the majority of existing studies focus on road maps in cities. Inherently, existing algorithms are not guaranteed to work on unusual geospatial sites, such as an airport tarmac, pedestrianized paths and shortcuts, or animal migration routes, etc. Moreover, deep learning has not been explored well enough for such tasks. This paper introduces COLTRANE, ConvolutiOnaL TRAjectory NEtwork, a novel deep map inference framework which operates on GPS trajectories collected in various environments. This framework includes an Iterated Trajectory Mean Shift (ITMS) module to localize road centerlines, which copes with noisy GPS data points. Convolutional Neural Network trained on our novel trajectory descriptor is then introduced into our framework to detect and accurately classify junctions for refinement of the road maps. COLTRANE yields up to 37% improvement in F1 scores over existing methods on two distinct real-world datasets: city roads and airport tarmac.