# Coverage and Bias of Street View Imagery in Mapping the Urban Environment arXiv: https://arxiv.org/abs/2409.15386 ยท HTML: https://mlsanity.com/paper/2409.15386/ ## Key facts - Cited by 12 papers in the map (data through 14 Oct 2026). - Builds on 4 of its 83 references that are in the map. - First posted on arXiv 22 Sep 2024. ## Builds on (most cited first, 4 of 4) - [To use or not to use proprietary street view images in (health and place) research? That is the question](https://mlsanity.com/paper/2402.11504/) (2024) - [A citizen science toolkit to collect human perceptions of urban environments using open street view images](https://mlsanity.com/paper/2403.00174/) (2024) - [Estimating building energy efficiency from street view imagery, aerial imagery, and land surface temperature data](https://mlsanity.com/paper/2206.02270/) (2022) - [A Saliency-Guided Street View Image Inpainting Framework for Efficient Last-Meters Wayfinding](https://mlsanity.com/paper/2205.06934/) (2022) ## Built on by (most cited first, 12 of 12) - [OpenFACADES: An Open Framework for Architectural Caption and Attribute Data Enrichment via Street View Imagery](https://mlsanity.com/paper/2504.02866/) (2025) - [Into the Unknown: Applying Inductive Spatial-Semantic Location Embeddings for Predicting Individuals' Mobility Beyond Visited Places](https://mlsanity.com/paper/2506.14070/) (2025) - [Earth Embeddings Reveal Diverse Urban Signals from Space](https://mlsanity.com/paper/2604.03456/) (2026) - [It is not always greener on the other side: Greenery perception across demographics and personalities in multiple cities](https://mlsanity.com/paper/2512.17186/) (2025) - [Street View Sociability: Interpretable Analysis of Urban Social Behavior Across 15 Cities](https://mlsanity.com/paper/2508.06342/) (2025) - [Using street view images and visual LLMs to predict heritage values for governance support: Risks, ethics, and policy implications](https://mlsanity.com/paper/2601.06056/) (2025) - [City landscape in sight: A crowdsourced framework for unlocking urban-scale window view perceptions from real estate imagery](https://mlsanity.com/paper/2606.15198/) (2026) - [AI vs Human Expert Reasoning: Assessing Agreements in Building Typology Predictions based on Street View Imagery](https://mlsanity.com/paper/2607.14756/) (2026) - [Cross-View Urban Sensing: Mapping Subjective Streetscape Perception via AlphaEarth Embeddings and Urban Context](https://mlsanity.com/paper/2608.16310/) (2026) - [You Cannot Photograph the Same Street Twice: Reliability Limits in Vision-Language Measurement of Urban Change](https://mlsanity.com/paper/2609.00649/) (2026) - [Seeing the City or Recognizing the Place? What Street-View Imagery Adds Beyond Existing Urban Data in VLM-Based Urban Sensing](https://mlsanity.com/paper/2610.00031/) (2026) - [A Decade of PhotoMappers: A Longitudinal Study of Crowdsourced Disaster Photos Geolocalization with Vision-Language Models and Geospatial Reasoning](https://mlsanity.com/paper/2610.15403/) (2026) ## Abstract (arXiv) Street View Imagery (SVI) has emerged as a valuable data form in urban studies, enabling new ways to map and sense urban environments. However, fundamental concerns regarding the representativeness, quality, and reliability of SVI remain underexplored, e.g. to what extent can cities be captured by such data and do data gaps result in bias. This research, positioned at the intersection of spatial data quality and urban analytics, addresses these concerns by proposing a novel and effective method to estimate SVI's element-level coverage in the urban environment. The method integrates the positional relationships between SVI and target elements, as well as the impact of physical obstructions. Expanding the domain of data quality to SVI, we introduce an indicator system that evaluates the extent of coverage, focusing on the completeness and frequency dimensions. Taking London as a case study, three experiments are conducted to identify potential biases in SVI's ability to cover and represent urban environmental elements, using building facades as an example. It is found that despite their high availability along urban road networks, Google Street View covers only 62.4 % of buildings in the case study area. The average facade coverage per building is 12.4 %. SVI tends to over-represent non-residential buildings, thus possibly resulting in biased analyses, and its coverage of environmental elements is position-dependent. The research also highlights the variability of SVI coverage under different data acquisition practices and proposes an optimal sampling interval range of 50-60 m for SVI collection. The findings suggest that while SVI offers valuable insights, it is no panacea - its application in urban research requires careful consideration of data coverage and element-level representativeness to ensure reliable results.