  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.
