Analysis of the Uniqueness and Similarity of City Landscapes Based on Deep Style Learning

The city landscape is largely related to the design concept and aesthetics of planners. Influenced by globalization, planners and architects have borrowed from available designs, resulting in the “one city with a thousand faces” phenomenon. In order to create a unique urban landscape, they need to f...

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Autores principales:	Ling Zhao, Li Luo, Bo Li, Liyan Xu, Jiawei Zhu, Silu He, Haifeng Li
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	city landscape social media images Flickr style distance visual similarity Geography (General) G1-922
Acceso en línea:	https://doaj.org/article/9730f137d6644fb4bbe4370ee4f4dd0f
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id	oai:doaj.org-article:9730f137d6644fb4bbe4370ee4f4dd0f
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spelling	oai:doaj.org-article:9730f137d6644fb4bbe4370ee4f4dd0f2021-11-25T17:52:50ZAnalysis of the Uniqueness and Similarity of City Landscapes Based on Deep Style Learning10.3390/ijgi101107342220-9964https://doaj.org/article/9730f137d6644fb4bbe4370ee4f4dd0f2021-10-01T00:00:00Zhttps://www.mdpi.com/2220-9964/10/11/734https://doaj.org/toc/2220-9964The city landscape is largely related to the design concept and aesthetics of planners. Influenced by globalization, planners and architects have borrowed from available designs, resulting in the “one city with a thousand faces” phenomenon. In order to create a unique urban landscape, they need to focus on local urban characteristics while learning new knowledge. Therefore, it is particularly important to explore the characteristics of cities’ landscapes. Previous researchers have studied them from different perspectives through social media data such as element types and feature maps. They only considered the content information of a image. However, social media images themselves have a “photographic cultural” character, which affects the city character. Therefore, we introduce this characteristic and propose a deep style learning for the city landscape method that can learn the global landscape features of cities from massive social media images encoded as vectors called city style features (CSFs). We find that CSFs can describe two landscape features: (1) intercity landscape features, which can quantitatively assess the similarity of intercity landscapes (we find that cities in close geographical proximity tend to have greater visual similarity to each other), and (2) intracity landscape features, which contain the inherent style characteristics of cities, and more fine-grained internal-city style characteristics can be obtained through cluster analysis. We validate the effectiveness of the above method on over four million Flickr social media images. The method proposed in this paper also provides a feasible approach for urban style analysis.Ling ZhaoLi LuoBo LiLiyan XuJiawei ZhuSilu HeHaifeng LiMDPI AGarticlecity landscapesocial media imagesFlickrstyle distancevisual similarityGeography (General)G1-922ENISPRS International Journal of Geo-Information, Vol 10, Iss 734, p 734 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	city landscape social media images Flickr style distance visual similarity Geography (General) G1-922
spellingShingle	city landscape social media images Flickr style distance visual similarity Geography (General) G1-922 Ling Zhao Li Luo Bo Li Liyan Xu Jiawei Zhu Silu He Haifeng Li Analysis of the Uniqueness and Similarity of City Landscapes Based on Deep Style Learning
description	The city landscape is largely related to the design concept and aesthetics of planners. Influenced by globalization, planners and architects have borrowed from available designs, resulting in the “one city with a thousand faces” phenomenon. In order to create a unique urban landscape, they need to focus on local urban characteristics while learning new knowledge. Therefore, it is particularly important to explore the characteristics of cities’ landscapes. Previous researchers have studied them from different perspectives through social media data such as element types and feature maps. They only considered the content information of a image. However, social media images themselves have a “photographic cultural” character, which affects the city character. Therefore, we introduce this characteristic and propose a deep style learning for the city landscape method that can learn the global landscape features of cities from massive social media images encoded as vectors called city style features (CSFs). We find that CSFs can describe two landscape features: (1) intercity landscape features, which can quantitatively assess the similarity of intercity landscapes (we find that cities in close geographical proximity tend to have greater visual similarity to each other), and (2) intracity landscape features, which contain the inherent style characteristics of cities, and more fine-grained internal-city style characteristics can be obtained through cluster analysis. We validate the effectiveness of the above method on over four million Flickr social media images. The method proposed in this paper also provides a feasible approach for urban style analysis.
format	article
author	Ling Zhao Li Luo Bo Li Liyan Xu Jiawei Zhu Silu He Haifeng Li
author_facet	Ling Zhao Li Luo Bo Li Liyan Xu Jiawei Zhu Silu He Haifeng Li
author_sort	Ling Zhao
title	Analysis of the Uniqueness and Similarity of City Landscapes Based on Deep Style Learning
title_short	Analysis of the Uniqueness and Similarity of City Landscapes Based on Deep Style Learning
title_full	Analysis of the Uniqueness and Similarity of City Landscapes Based on Deep Style Learning
title_fullStr	Analysis of the Uniqueness and Similarity of City Landscapes Based on Deep Style Learning
title_full_unstemmed	Analysis of the Uniqueness and Similarity of City Landscapes Based on Deep Style Learning
title_sort	analysis of the uniqueness and similarity of city landscapes based on deep style learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/9730f137d6644fb4bbe4370ee4f4dd0f
work_keys_str_mv	AT lingzhao analysisoftheuniquenessandsimilarityofcitylandscapesbasedondeepstylelearning AT liluo analysisoftheuniquenessandsimilarityofcitylandscapesbasedondeepstylelearning AT boli analysisoftheuniquenessandsimilarityofcitylandscapesbasedondeepstylelearning AT liyanxu analysisoftheuniquenessandsimilarityofcitylandscapesbasedondeepstylelearning AT jiaweizhu analysisoftheuniquenessandsimilarityofcitylandscapesbasedondeepstylelearning AT siluhe analysisoftheuniquenessandsimilarityofcitylandscapesbasedondeepstylelearning AT haifengli analysisoftheuniquenessandsimilarityofcitylandscapesbasedondeepstylelearning
_version_	1718411874824880128

Analysis of the Uniqueness and Similarity of City Landscapes Based on Deep Style Learning

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