Summary of Coverage and Bias Of Street View Imagery in Mapping the Urban Environment, by Zicheng Fan et al.
Coverage and Bias of Street View Imagery in Mapping the Urban Environment
by Zicheng Fan, Chen-Chieh Feng, Filip Biljecki
First submitted to arxiv on: 22 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel method to estimate the coverage of Street View Imagery (SVI) at an element level, addressing concerns about the representativeness, quality, and reliability of SVI in urban studies. The method integrates positional relationships between SVI and target elements, considering physical obstructions. An indicator system is introduced to evaluate coverage completeness and frequency, focusing on building facades as an example. Three experiments are conducted using London as a case study, revealing that Google Street View covers only 62.4% of buildings, with non-residential buildings over-represented. The research highlights the variability of SVI coverage under different data acquisition practices and proposes an optimal sampling interval range for SVI collection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how good Street View Imagery is at showing what’s in cities. It turns out that even though there are a lot of photos, some things like buildings might not be fully captured. The researchers found that Google Street View only shows about 62% of buildings, and it likes to show big buildings more than small ones. They also discovered that the quality of the pictures can change depending on how they were taken. This is important because we need accurate information to understand our cities. |
