The Oxford Road Boundaries is a dataset designed for training and testing machine-learning-based road-boundary detection and inference approaches.
The authors 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, the authors used a vision-based localiser to project labels from the annotated datasets to other traversals at different times and weather conditions.
As a result, the dataset consists of 62,605 labelled samples, of which 47,639 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.