Classifying Bicycle Infrastructure Using On-Bike Street-Level Images

TL;DR

This paper introduces a novel system that classifies cycling infrastructure from on-bike smartphone images, achieving high accuracy and robustness, to help improve city cycling networks and promote sustainable transportation.

Contribution

It presents the first approach to classify cycling infrastructure using street-level images from bike-mounted phones with temporal analysis for robustness.

Findings

Achieved 95.38% accuracy in infrastructure classification.

Model outperformed non-temporal models by 7.55%.

Maintained robustness with 90% image feature absence.

Abstract

While cycling offers an attractive option for sustainable transportation, many potential cyclists are discouraged from taking up cycling due to the lack of suitable and safe infrastructure. Efficiently mapping cycling infrastructure across entire cities is necessary to advance our understanding of how to provide connected networks of high-quality infrastructure. Therefore we propose a system capable of classifying available cycling infrastructure from on-bike smartphone camera data. The system receives an image sequence as input, temporally analyzing the sequence to account for sparsity of signage. The model outputs cycling infrastructure class labels defined by a hierarchical classification system. Data is collected via participant cyclists covering 7,006Km across the Greater Melbourne region that is automatically labeled via a GPS and OpenStreetMap database matching algorithm. The proposed model achieved an accuracy of 95.38%, an increase in performance of 7.55% compared to the non-temporal model. The model demonstrated robustness to extreme absence of image features where the model lost only 6.6% in accuracy after 90% of images being replaced with blank images. This work is the first to classify cycling infrastructure using only street-level imagery collected from bike-mounted mobile phone cameras, while demonstrating robustness to feature sparsity via long temporal sequence analysis.