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Assessing streetscape greenery with deep neural network using Google Street View.

Taishin Kameoka1, Atsuhiko Uchida1, Yu Sasaki1

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This study introduces a new method for measuring urban greenery using Google Street View images. It improves upon existing techniques for assessing green view index in cities.

Keywords:
GISGoogle Street Viewchopped picture methoddeep learninggreen view indexurban greenery

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Area of Science:

  • Urban planning
  • Environmental science
  • Computer vision

Background:

  • Urban greenery is crucial for ecological, aesthetic, public health, and social science applications.
  • Recent studies leverage big data and automated image recognition for urban greenery analysis.
  • Existing automated methods struggle with distinguishing artificial green objects and incur high training costs.

Purpose of the Study:

  • To develop a more accurate and cost-effective method for quantifying urban greenery.
  • To address limitations in current automated image recognition techniques for urban streetscapes.
  • To enhance the assessment of the green view index in urban environments.

Main Methods:

  • Application of a patch-based semantic segmentation approach.
  • Utilizing Google Street View imagery.
  • Implementation of the 'chopped picture method' for image analysis.

Main Results:

  • A novel method for determining the green view index in urban areas was developed.
  • The proposed technique aims to overcome the limitations of existing automated image recognition methods.
  • The approach offers a potentially more efficient and accurate way to analyze urban greenery.

Conclusions:

  • The developed method is expected to improve the quantitative analysis of urban greenery.
  • This research contributes to expanding the scope of studies on urban greenery across various disciplines.
  • The findings facilitate better urban planning and environmental management through improved greenery assessment.