  In this paper we present the Oxford Road Boundaries Dataset, designed for
training and testing machine-learning-based road-boundary detection and
inference approaches. We have hand-annotated two of the 10 km-long forays from
the Oxford Robotcar Dataset and generated from other forays several thousand
further examples with semi-annotated road-boundary masks. To boost the number
of training samples in this way, we used a vision-based localiser to project
labels from the annotated datasets to other traversals at different times and
weather conditions. As a result, we release 62605 labelled samples, of which
47639 samples are curated. Each of these samples contains both raw and
classified masks for left and right lenses. Our data contains images from a
diverse set of scenarios such as straight roads, parked cars, junctions, etc.
Files for download and tools for manipulating the labelled data are available
at: oxford-robotics-institute.github.io/road-boundaries-dataset
