Coverage and Bias of Street View Imagery in Mapping the Urban Environment

2024-09-22

arXiv.org

AI Summary

Existing street-view imagery (SVI) coverage assessments overlook spatial relationships and physical occlusion, leading to inaccurate evaluations of urban element-level completeness and bias. Method: We propose a novel coverage quantification framework integrating spatial proximity modeling and occlusion analysis, coupled with a multidimensional quality metric system assessing both completeness and sampling frequency. Using London as a case study, we conduct spatial relationship modeling, occlusion-aware analysis, and geographically weighted statistics. Results: Google Street View covers only 62.4% of buildings, with façade coverage as low as 12.4%; significant functional-type biases exist (e.g., oversampling of non-residential buildings) and strong location-dependent coverage patterns. We empirically determine 50–60 meters as the optimal image capture interval. This work establishes the first element-level SVI coverage assessment paradigm, providing a reusable methodological foundation for urban remote sensing data quality evaluation.

Technology Category

• Computer Vision: Scene Analysis & Understanding
• Search and Optimization: Evaluation and Analysis
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Web data visualization

Abstract

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.

Problem

• Urban Representation
• Visual Bias
• Street View Photography

Innovation

• Street View Imagery
• Urban Coverage Assessment
• Bias Evaluation