Representing place locales using scene elements

Locale is one of the basic elements of place, referring to the physical settings and visual appearance of a place. Understanding and representing a locale is of great importance in terms of human perception and human activity. However, taking a quantitative measurement of the visual appearance of ur...

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Bibliographic Details
Published in:Computers, environment and urban systems Vol. 71; pp. 153 - 164
Main Authors: Zhang, Fan, Zhang, Ding, Liu, Yu, Lin, Hui
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 01.09.2018
Elsevier Science Ltd
Subjects:
BASIC (programming language)
Deep learning
Geographic information science
Geography
Image segmentation
Ontology
Place formalization
Quantitative representation
Representations
Scene ontology
Social factors
Socioeconomic data
Street-level images
Urban environments
Visual perception
Quantitative representation
Deep learning
Scene ontology
Place formalization
Street-level images
ISSN:0198-9715, 1873-7587
Online Access:Get full text
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Summary:Locale is one of the basic elements of place, referring to the physical settings and visual appearance of a place. Understanding and representing a locale is of great importance in terms of human perception and human activity. However, taking a quantitative measurement of the visual appearance of urban environment has proven to be challenging because visual information is inherently ambiguous and semantically impoverished. To mitigate this issue, this paper employs street-level images as the proxy for urban physical appearance, utilizes the recently developed image semantic segmentation techniques to parse an urban scene into scene elements, and proposes a framework for locale representation using scene elements. The framework is composed of two major components: street scene ontology and street visual descriptor, which are aimed at street scene qualitative understanding and quantitative representation respectively. A case study is developed to demonstrate the application and advantage of the street scene ontology and street visual descriptor. A series of quantitative analyses demonstrates the ability and great potential of the framework for investigating the connections between place and other socioeconomic factors. •A framework for locale representation using scene elements that obtained from street-level images using deep learning model•An urban scene ontology towards urban physical environment understanding is proposed.•A street visual descriptor for street scene quantitative representation is proposed.
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ISSN:0198-9715
1873-7587
DOI:10.1016/j.compenvurbsys.2018.05.005