# Assessing bikeability with street view imagery and computer vision

arXiv: https://arxiv.org/abs/2105.08499 · HTML: https://mlsanity.com/paper/2105.08499/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 145 references that are in the map.
- First posted on arXiv 13 May 2021.

## Builds on (most cited first, 8 of 8)

- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks](https://mlsanity.com/paper/1611.01890/) (2016)
- [In-Place Activated BatchNorm for Memory-Optimized Training of DNNs](https://mlsanity.com/paper/1712.02616/) (2017)
- [Deep Learning the City : Quantifying Urban Perception At A Global Scale](https://mlsanity.com/paper/1608.01769/) (2016)
- [GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing](https://mlsanity.com/paper/1907.04433/) (2019)
- [Estimating city-level travel patterns using street imagery: a case study of using Google Street View in Britain](https://mlsanity.com/paper/1802.02915/) (2018)
- [Roofpedia: Automatic mapping of green and solar roofs for an open roofscape registry and evaluation of urban sustainability](https://mlsanity.com/paper/2012.14349/) (2020)
- [Classification of Urban Morphology with Deep Learning: Application on Urban Vitality](https://mlsanity.com/paper/2105.09908/) (2021)

## Built on by (most cited first, 15 of 15)

- [ZenSVI: An Open-Source Software for the Integrated Acquisition, Processing and Analysis of Street View Imagery Towards Scalable Urban Science](https://mlsanity.com/paper/2412.18641/) (2024)
- [Computer vision-enriched discrete choice models, with an application to residential location choice](https://mlsanity.com/paper/2308.08276/) (2023)
- [Classifying Bicycle Infrastructure Using On-Bike Street-Level Images](https://mlsanity.com/paper/2410.19194/) (2024)
- [Accessibility Scout: Personalized Accessibility Scans of Built Environments](https://mlsanity.com/paper/2507.23190/) (2025)
- [AutoLTS: Automating Cycling Stress Assessment via Contrastive Learning and Spatial Post-processing](https://mlsanity.com/paper/2308.07580/) (2023)
- [Images in Discrete Choice Modeling: Addressing Data Isomorphism in Multi-Modality Inputs](https://mlsanity.com/paper/2312.14724/) (2023)
- [Predicting the impact of urban change in pedestrian and road safety](https://mlsanity.com/paper/2202.01781/) (2022)
- [Scoring Cycling Environments Perceived Safety using Pairwise Image Comparisons](https://mlsanity.com/paper/2307.13397/) (2023)
- [How does spatial structure affect psychological restoration? A method based on Graph Neural Networks and Street View Imagery](https://mlsanity.com/paper/2311.17361/) (2023)
- [Vehicle detection from GSV imagery: Predicting travel behaviour for cycling and motorcycling using Computer Vision](https://mlsanity.com/paper/2508.12794/) (2025)
- [URBAN-SPIN: A street-level bikeability index to inform design implementations in historical city centres](https://mlsanity.com/paper/2602.10124/) (2026)
- [The Spectrascapes Dataset: Street-view imagery beyond the visible captured using a mobile platform](https://mlsanity.com/paper/2604.13315/) (2026)
- [Learning to See Like Humans: Gaze-Aligned Cycling Safety Prediction](https://mlsanity.com/paper/2605.24040/) (2026)
- [Examining the Associations between Visual and Non-Visual Elements and Cyclists' Route Choices for Various Trip Purposes](https://mlsanity.com/paper/2607.15808/) (2026)
- [Vision Transformer Ensembles for Panoramic Street Segmentation](https://mlsanity.com/paper/2610.06063/) (2026)

## Abstract (arXiv)

Studies evaluating bikeability usually compute spatial indicators shaping cycling conditions and conflate them in a quantitative index. Much research involves site visits or conventional geospatial approaches, and few studies have leveraged street view imagery (SVI) for conducting virtual audits. These have assessed a limited range of aspects, and not all have been automated using computer vision (CV). Furthermore, studies have not yet zeroed in on gauging the usability of these technologies thoroughly. We investigate, with experiments at a fine spatial scale and across multiple geographies (Singapore and Tokyo), whether we can use SVI and CV to assess bikeability comprehensively. Extending related work, we develop an exhaustive index of bikeability composed of 34 indicators. The results suggest that SVI and CV are adequate to evaluate bikeability in cities comprehensively. As they outperformed non-SVI counterparts by a wide margin, SVI indicators are also found to be superior in assessing urban bikeability, and potentially can be used independently, replacing traditional techniques. However, the paper exposes some limitations, suggesting that the best way forward is combining both SVI and non-SVI approaches. The new bikeability index presents a contribution in transportation and urban analytics, and it is scalable to assess cycling appeal widely.