打印

作者

Jialyu He;Jinbao Zhang;Yao Yao;Xia Li

来源

CITIES,Vol.135,Issue1,Article 104189

语言

英文

关键字

作者单位

Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, Guangdong province, P.R. China;School of Geography and Information Engineering, China University of Geosciences, Wuhan, Hubei 430078, P.R. China;Center for Spatial Information Science, The University of Tokyo, Kashiwa-shi, Chiba 277-8568, Japan;Key Lab. of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, 500 Dongchuan Rd, Shanghai 200241, P.R. China;Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, Guangdong province, P.R. China;School of Geography and Information Engineering, China University of Geosciences, Wuhan, Hubei 430078, P.R. China;Center for Spatial Information Science, The University of Tokyo, Kashiwa-shi, Chiba 277-8568, Japan;Key Lab. of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, 500 Dongchuan Rd, Shanghai 200241, P.R. China;State Key laboratory of urban and regional Ecology, Research Center for Eco-environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China;University of Chinese Academy of Sciences, Beijing 100049, China;Beijing-Tianjin-Hebei Urban Megaregion National Observation and Research Station for Eco-Environmental Change, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China;Xiongan Institute of Innovation, Xiongan New Area, 071000, China;College of Land Science and Technology, China Agricultural University, Beijing 100083, China;Key Laboratory of Remote Sensing for Agri-Hazards, Ministry of Agriculture and Rural Affairs, Beijing 100083, China;Department of Geology and Atmosphere Sciences, Iowa State University, IA 50014, USA;School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China;Department of Geography, The University of Hong Kong, Hong Kong, China;Beijing Municipal Institute of City Planning and Design, Beijing 100045, China;AI for Earth Lab, Cross-Strait Institute, Tsinghua University, Beijing 100084, China;School of Architecture, Tsinghua University, Beijing 100084, China;University of Alicante, Spain;Xi'an Jiaotong-Liverpool University, China;College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China;Technical University of Munich, Chair of Data Science in Earth Observation, Arcisstraße 21, Munich 80333, Germany;German Aerospace Center, Remote Sensing Technology Institute, Muenchener Straße 20, Weßling 82234, Germany;Center for Spatial Information Science, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi, Chiba 277-8568, Japan;LocationMind Inc., 3-5-2 Iwamotocho, Chiyoda-ku, Tokyo 101-0032, Japan;SUSTech-UTokyo Joint Research Center on Super Smart City, Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen, China;College of Management and Economics, Tianjin University, Tianjin, China;School of Urban Planning & Design, Peking University, Shenzhen, China

摘要

Accurate and efficient assessment of large-scale urban renewal potential is an indispensable prerequisite for managing and facilitating projects. However, few studies consider the built environment when assessing urban renewal potential because it is difficult to measure. Street view images can show the physical setting of a place for humans to perceive the built environment. Hence, we separately extracted emotional and visual perceptions from street view images to construct a new comprehensive indicator set to assess multi-class urban renewal potentials. To establish the assessment model, we applied a backpropagation neural network based on the presence and background learning (PBL-BPNN). The renewal potential assessment based on the proposed indicator set can reach the highest accuracy. Emotional perceptions contribute more to assessing renewal potential than visual perceptions because they are more consistent in portraying the blighted built environment. Emotionally, the ratings of safety, boring, depression, and lively are stable in the blighted built environment. Visually, greenness and imageability often remain at lower values, highlighting the importance of greenspace and urban furniture in determining urban renewal. Furthermore, multi-class renewal potentials can be used for scenario analysis by assuming different renewal intentions. The results can support governments and planners in making efficient urban renewal decisions.