# Artifacts of Idiosyncracy in Global Street View Data

arXiv: https://arxiv.org/abs/2505.11046 · HTML: https://mlsanity.com/paper/2505.11046/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 61 references that are in the map.
- First posted on arXiv 16 May 2025.

## Builds on (most cited first, 16 of 16)

- [Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrieval](https://mlsanity.com/paper/2004.01804/) (2020)
- [GSV-Cities: Toward Appropriate Supervised Visual Place Recognition](https://mlsanity.com/paper/2210.10239/) (2022)
- [Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US](https://mlsanity.com/paper/1702.06683/) (2017)
- [Deep Learning the City : Quantifying Urban Perception At A Global Scale](https://mlsanity.com/paper/1608.01769/) (2016)
- [The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale](https://mlsanity.com/paper/1909.04422/) (2019)
- [Adaptive-Attentive Geolocalization from few queries: a hybrid approach](https://mlsanity.com/paper/2010.06897/) (2020)
- [D2-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios](https://mlsanity.com/paper/1904.01975/) (2019)
- [Nexar Dashcam Collision Prediction Dataset and Challenge](https://mlsanity.com/paper/2503.03848/) (2025)
- [Computer Vision for Road Imaging and Pothole Detection: A State-of-the-Art Review of Systems and Algorithms](https://mlsanity.com/paper/2204.13590/) (2022)
- [Take a Look Around: Using Street View and Satellite Images to Estimate House Prices](https://mlsanity.com/paper/1807.07155/) (2018)
- [Detecting disparities in police deployments using dashcam data](https://mlsanity.com/paper/2305.15210/) (2023)
- [CityPulse: Fine-Grained Assessment of Urban Change with Street View Time Series](https://mlsanity.com/paper/2401.01107/) (2024)
- [Urban Mosaic: Visual Exploration of Streetscapes Using Large-Scale Image Data](https://mlsanity.com/paper/2008.13321/) (2020)
- [Training a Convolutional Neural Network for Appearance-Invariant Place Recognition](https://mlsanity.com/paper/1505.07428/) (2015)
- [Analysing and Organising Human Communications for AI Fairness-Related Decisions: Use Cases from the Public Sector](https://mlsanity.com/paper/2404.00022/) (2024)
- [Paved or unpaved? A Deep Learning derived Road Surface Global Dataset from Mapillary Street-View Imagery](https://mlsanity.com/paper/2410.19874/) (2024)

## Built on by (most cited first, 1 of 1)

- [You Cannot Photograph the Same Street Twice: Reliability Limits in Vision-Language Measurement of Urban Change](https://mlsanity.com/paper/2609.00649/) (2026)

## Abstract (arXiv)

Street view data is increasingly being used in computer vision applications in recent years. Machine learning datasets are collected for these applications using simple sampling techniques. These datasets are assumed to be a systematic representation of cities, especially when densely sampled. Prior works however, show that there are clear gaps in coverage, with certain cities or regions being covered poorly or not at all. Here we demonstrate that a cities' idiosyncracies, such as city layout, may lead to biases in street view data for 28 cities across the globe, even when they are densely covered. We quantitatively uncover biases in the distribution of coverage of street view data and propose a method for evaluation of such distributions to get better insight in idiosyncracies in a cities' coverage. In addition, we perform a case study of Amsterdam with semi-structured interviews, showing how idiosyncracies of the collection process impact representation of cities and regions and allowing us to address biases at their source.