Synthesizing location semantics from street view images to improve urban land-use classification

Fang, Fang; Yu, Yafang; Li, Shengwen; Zuo, Zhengxing; Liu, Yuanyuan; Wan, Bo; Luo, Zhiyong

doi:10.1080/13658816.2020.1831515

Cited by 18 publications

(2 citation statements)

References 31 publications

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“…Street view imagery (SVI) has proven useful for many applications; see Biljecki and Ito (2021) for a review in urban analytics and geographic information science. As an example, Fang et al (2021) use SVI to produce land-use classification and land-use mapping, critical tools for urban planning and environmental research. In Chen et al (2022) and Blanc (2022), the authors use SVI to perform information extraction from images to automate the data-collection process, improving the quoting process for customers.…”

Section: Introductionmentioning

confidence: 99%

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A representation-learning approach for insurance pricing with images

Blier-Wong,

Lamontagne,

Marceau

2024

ASTIN Bull.

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Unstructured data are a promising new source of information that insurance companies may use to understand their risk portfolio better and improve the customer experience. However, these novel data sources are difficult to incorporate into existing ratemaking frameworks due to the size and format of the unstructured data. This paper proposes a framework to use street view imagery within a generalized linear model. To do so, we use representation learning to extract an embedding vector containing useful information from the image. This embedding is dense and low dimensional, making it appropriate to use within existing ratemaking models. We find that there is useful information included in street view imagery to predict the frequency of claims for certain types of perils. This model can be used as in a ratemaking framework but also opens the door to future empirical research on attempting to extract which characteristics within the image leads to increased or decreased predicted claim frequencies. Throughout, we discuss the practical difficulties (technical and social) of using this type of data for insurance pricing.

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Section: Introductionmentioning

confidence: 99%

“…As an example, Fang et al. (2021) use SVI to produce land-use classification and land-use mapping, critical tools for urban planning and environmental research. In Chen et al.…”

Section: Introductionmentioning

confidence: 99%