# Classifying Bicycle Infrastructure Using On-Bike Street-Level Images

arXiv: https://arxiv.org/abs/2410.19194 · HTML: https://mlsanity.com/paper/2410.19194/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 24 Oct 2024.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://mlsanity.com/paper/2301.00808/) (2023)
- [Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone](https://mlsanity.com/paper/1801.09454/) (2018)
- [Assessing bikeability with street view imagery and computer vision](https://mlsanity.com/paper/2105.08499/) (2021)
- [Road Segmentation in SAR Satellite Images with Deep Fully-Convolutional Neural Networks](https://mlsanity.com/paper/1802.01445/) (2018)
- [Fast building segmentation from satellite imagery and few local labels](https://mlsanity.com/paper/2206.05377/) (2022)
- [Embedding Structured Contour and Location Prior in Siamesed Fully Convolutional Networks for Road Detection](https://mlsanity.com/paper/1905.01575/) (2019)
- [Infrastructure Quality Assessment in Africa using Satellite Imagery and Deep Learning](https://mlsanity.com/paper/1806.00894/) (2018)
- [CI-dataset and DetDSCI methodology for detecting too small and too large critical infrastructures in satellite images: Airports and electrical substations as case study](https://mlsanity.com/paper/2105.11844/) (2021)
- [BikeDNA: A Tool for Bicycle Infrastructure Data & Network Assessment](https://mlsanity.com/paper/2303.01223/) (2023)

## Built on by (most cited first, 2 of 2)

- [Mapping Woody Vegetation from Multi-Source Imagery and Prediction Fusion for Enhanced Data Efficiency and Accuracy](https://mlsanity.com/paper/2608.26471/) (2026)
- [Learning Woody Clearing With Loss Alignment for Zero-Shot Regrowth and Woody Segmentation](https://mlsanity.com/paper/2608.26489/) (2026)

## Abstract (arXiv)

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.