The Oxford Road Boundaries Dataset

TL;DR

The Oxford Road Boundaries Dataset provides a large, diverse set of annotated images for training and testing machine learning models in road-boundary detection, leveraging semi-automatic annotation and projection techniques.

Contribution

This work introduces a new extensive dataset with over 62,000 labeled samples for road-boundary detection, including semi-annotated data and tools for data manipulation.

Findings

Generated 62,605 labeled samples, with 47,639 curated.

Diverse scenarios including junctions and parked cars.

Semi-automatic annotation enhances dataset scale.

Abstract

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