Quantifying physical and psychological perceptions of urban scenes using deep learning. (December 2021)

• Record Type: Journal Article
• Authors: Zhang, Yonglin; Li, Shanlin; Dong, Rencai; Deng, Hongbing; Fu, Xiao; Wang, Chenxing; Yu, Tianshu; Jia, Tianxia; Zhao, Jingzhu
• Is Part Of: Land use policy. Volume 111(2021)
• Publication Date: 2021-12
• Subjects: LSIs Landsense Indices -- VI view index -- NFIs number of foreground instances -- PP-GSV MIT Place Pulse Google Street View -- BJ-TSV Beijing Tencent Street View -- EULUC Essential Urban Land Use Categories -- Panoptic-FPN Panoptic Feature Pyramid Networks -- ML machine learning -- DL deep learning; Complex perceptions -- Cityscapes -- Image big data -- Deep learning -- Massive street-view datasets -- Human-oriented; Land use -- Periodicals; Land use -- Government policy -- Periodicals; Sol, Utilisation du -- Périodiques; Sol, Utilisation du -- Politique gouvernementale -- Périodiques; Electronic journals; 333.7305
• DOI: 10.1016/j.landusepol.2021.105762
• Languages: English
• ISSNs: 0264-8377

Abstract

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. Highlights: This study maps and visualizes landsenses indices (LSIs) in central Beijing. This study proposes transfer learning models to quantify scene perceptions. This study models the connections between physical and psychological perceptions.