Quantifying physical and psychological perceptions of urban scenes using deep learning

Yonglin Zhang, Shanlin Li, Rencai Dong, Hongbing Deng, Xiao Fu, Chenxing Wang, Tianshu Yu, Tianxia Jia and Jingzhu Zhao

Land Use Policy, 2021, vol. 111, issue C

Abstract: The complicated relationship between urban scenes and public perceptions has long been a concern in many disciplines. Previous studies have lacked human-oriented technical paths and high-throughput datasets to quantify physical and psychological perceptions in different land-use scenarios. This paper adopts a novel transfer learning approach to quantify the six types of landsense indices (LSIs) as psychological perception metrics and employs panoptic segmentation to parameterize the view index (VI) and the number of foreground instances (NFIs) as physical perception measures. Then, a quantitative analysis is conducted in Beijing’s six Ring Road areas, and the connections between people’s physical and psychological perceptions of heterogeneous land use are explored. The landsense maps can depict the distribution of LSIs and facilitate the understanding of complex perceptions distributed at a large scale. The regression model shows that natural landscapes (trees, grasses, and mountains) in the Beijing built-up area exhibit an overall positive performance. Moreover, for several block-level land uses, industrial scenery is related to overall negative psychological feelings. Parks and green spaces are positively related to psychological perceptions, because of the greater exposure opportunities to natural landscapes for residents. The framework in this research has potential in assisting urban planning and land-use management, and it enriches the datasets with extensive information, thereby improving the psychological perceptions of urban scenes from residents’ perspectives. The novel approaches in this paper take a step forward in quantifying and understanding the public perceptions of urban landscapes.

Keywords: Complex perceptions; Cityscapes; Image big data; Deep learning; Massive street-view datasets; Human-oriented (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0264837721004853
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:lauspo:v:111:y:2021:i:c:s0264837721004853

DOI: 10.1016/j.landusepol.2021.105762

Access Statistics for this article

Land Use Policy is currently edited by Jaap Zevenbergen

More articles in Land Use Policy from Elsevier
Bibliographic data for series maintained by Joice Jiang ().