Classification of urban morphology with deep learning: Application on urban vitality

Computers, Environment and Urban Systems - Tập 90 - Trang 101706 - 2021
Wangyang Chen1, Abraham Noah Wu1, Filip Biljecki1,2
1Department of Architecture, National University of Singapore, Singapore
2Department of Real Estate, National University of Singapore, Singapore

Tài liệu tham khảo

Alexander, 1977 Baran, 2008, Space syntax and walking in a new urbanist and suburban neighbourhoods, Journal of Urban Design, 13, 5, 10.1080/13574800701803498 Barrington-Leigh, 2017, The world’s user-generated road map is more than 80% complete, PLoS One, 12, 10.1371/journal.pone.0180698 Berghauser Pont, 2007, The spacemate: Density and the typomorphology of the urban fabric, 11 Berghauser-Pont, 2010 Biljecki, 2020, Exploration of open data in Southeast Asia to generate 3D building models, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, VI-4, 37, 10.5194/isprs-annals-VI-4-W1-2020-37-2020 Bocher, 2018, A geoprocessing framework to compute urban indicators: The mapuce tools chain, Urban Climate, 24, 153, 10.1016/j.uclim.2018.01.008 Boeing, 2017, Osmnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks, Computers, Environment and Urban Systems, 65, 126, 10.1016/j.compenvurbsys.2017.05.004 Boeing, 2020, A multi-scale analysis of 27,000 urban street networks: Every US city, town, urbanized area, and Zillow neighborhood, Environment and Planning B: Urban Analytics and City Science, 47, 590 Boeing, 2021, Spatial information and the legibility of urban form: Big data in urban morphology, International Journal of Information Management, 56, 102013, 10.1016/j.ijinfomgt.2019.09.009 Botta, 2021, Modelling urban vibrancy with mobile phone and openstreetmap data, PLoS One, 16, 10.1371/journal.pone.0252015 Canziani, 2016, An analysis of deep neural network models for practical applications, arXiv abs/1605.07678 Cao, 2019, Comparison of approaches for urban functional zones classification based on multi-source geospatial data: A case study in Yuzhong District, Chongqing, China, Sustainability, 11, 660, 10.3390/su11030660 Chan, 2011, Urban road networks—Spatial networks with universal geometric features?, The European Physical Journal B, 84, 563, 10.1140/epjb/e2011-10889-3 Chen, 2019, Identifying urban spatial structure and urban vibrancy in highly dense cities using georeferenced social media data, Habitat International, 89, 102005, 10.1016/j.habitatint.2019.102005 Crooks, 2016, User-generated big data and urban morphology, Built Environment, 42, 396, 10.2148/benv.42.3.396 Delclòs-Alió, 2018, Looking at Barcelona through Jane Jacobs’s eyes: Mapping the basic conditions for urban vitality in a Mediterranean conurbation, Land Use Policy, 75, 505, 10.1016/j.landusepol.2018.04.026 Ding, 2021, Towards generating network of bikeways from Mapillary data, Computers, Environment and Urban Systems, 88, 101632, 10.1016/j.compenvurbsys.2021.101632 Fleischmann, 2019, momepy: urban morphology measuring toolkit, Journal of Open Source Software, 4, 10.21105/joss.01807 Fleischmann, 2020, Morphological tessellation as a way of partitioning space: Improving consistency in urban morphology at the plot scale, Computers, Environment and Urban Systems, 80, 101441, 10.1016/j.compenvurbsys.2019.101441 Garbasevschi, 2021, Spatial factors influencing building age prediction and implications for urban residential energy modelling, Computers, Environment and Urban Systems, 88, 101637, 10.1016/j.compenvurbsys.2021.101637 Ge, 2018, Ghost city extraction and rate estimation in China based on npp-viirs night-time light data, ISPRS International Journal of Geo-Information, 7, 219, 10.3390/ijgi7060219 Gebru, 2017, Using deep learning and Google street view to estimate the demographic makeup of neighborhoods across the United States, Proceedings of the National Academy of Sciences, 114, 13108, 10.1073/pnas.1700035114 Han, 2020, Classification of urban street networks based on tree-like network features, Sustainability, 12, 628, 10.3390/su12020628 He, 2015 He, 2016 He, 2018, The impact of urban growth patterns on urban vitality in newly built-up areas based on an association rules analysis using geographical ‘big data’, Land Use Policy, 78, 726, 10.1016/j.landusepol.2018.07.020 Helbich, 2019, Using deep learning to examine street view green and blue spaces and their associations with geriatric depression in Beijing, China, Environment International, 126, 107, 10.1016/j.envint.2019.02.013 Hillier, 1996 Hillier, 1984 Jacobs, 1961 Jiang, 2002, Integration of space syntax into GIS: New perspectives for urban morphology, Transactions in GIS, 6, 295, 10.1111/1467-9671.00112 Jin, 2017, Evaluating cities’ vitality and identifying ghost cities in China with emerging geographical data, Cities, 63, 98, 10.1016/j.cities.2017.01.002 Jochem, 2021, Tools for mapping multi-scale settlement patterns of building footprints: An introduction to the R package foot, PLoS One, 16, 10.1371/journal.pone.0247535 Ke, 2017, Lightgbm: A highly efficient gradient boosting decision tree, Advances in Neural Information Processing Systems, 30, 3146 Kim, 2021, Decoding urban landscapes: Google street view and measurement sensitivity, Computers, Environment and Urban Systems, 88, 101626, 10.1016/j.compenvurbsys.2021.101626 Kim, 2018, Seoul’s wi-fi hotspots: Wi-fi access points as an indicator of urban vitality, Computers, Environment and Urban Systems, 72, 13, 10.1016/j.compenvurbsys.2018.06.004 Kim, 2020, Data-driven approach to characterize urban vitality: How spatiotemporal context dynamically defines Seoul’s nighttime, International Journal of Geographical Information Science, 34, 1235, 10.1080/13658816.2019.1694680 Landry, 2000, Urban vitality: A new source of urban competitiveness, Archis, 8 Li, J., Biljecki, F., 2019. The implementation of big data analysis in regulating online short-term rental business: A case of Airbnb in Beijing. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. IV-4/W9, 79–86. doi:https://doi.org/10.5194/isprs-annals-iv-4-w9-79-2019. Li, 2020, Urban morphology promotes urban vibrancy from the spatiotemporal and synergetic perspectives: A case study using multisource data in Shenzhen, China, Sustainability, 12, 4829, 10.3390/su12124829 Liu, 2010, Evaluation of urban vitality based on fuzzy matter-element mode, Geography and Geo-Information Science, 26, 73 Lloyd, 2019, Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets, Big Earth Data, 3, 108, 10.1080/20964471.2019.1625151 Lloyd, 2017, High resolution global gridded data for use in population studies, Scientific Data, 4, 10.1038/sdata.2017.1 Lopes, 2013, Public green space use and consequences on urban vitality: An assessment of European cities, Social Indicators Research, 113, 751, 10.1007/s11205-012-0106-9 Lynch, 1984 Ma, 2014, Diverse relationships between suomi-npp viirs night-time light and multi-scale socioeconomic activity, Remote Sensing Letters, 5, 652, 10.1080/2150704X.2014.953263 Marshall, 2005 Martino, 2021, Urban form and livability: Socioeconomic and built environment indicators, Buildings and Cities, 2, 10.5334/bc.82 Meng, 2019, Exploring the relationship between landscape characteristics and urban vibrancy: A case study using morphology and review data, Cities, 95, 102389, 10.1016/j.cities.2019.102389 Middel, 2019, Urban form and composition of street canyons: A human-centric big data and deep learning approach, Landscape and Urban Planning, 183, 122, 10.1016/j.landurbplan.2018.12.001 Moosavi, 2017, Urban morphology meets deep learning: Exploring urban forms in one million cities, town and villages across the planet, arXiv preprint Moudon, 1997, Urban morphology as an emerging interdisciplinary field, Urban Morphology, 1, 3, 10.51347/jum.v1i1.4047 Plater-Zyberk, 2003 Qu, 2019, Investigating the intensive redevelopment of urban central blocks using data envelopment analysis and deep learning: A case study of Nanjing, China, IEEE Access, 7, 109884, 10.1109/ACCESS.2019.2933691 Redmon, 2016 Ren Shannon, 1948, A mathematical theory of communication, The Bell System Technical Journal, 27, 379, 10.1002/j.1538-7305.1948.tb01338.x Snellen, 2002, Urban form, road network type, and mode choice for frequently conducted activities: A multilevel analysis using quasi-experimental design data, Environment and Planning A: Economy and Space, 34, 1207, 10.1068/a349 Southworth, 1995, Street standards and the shaping of suburbia, Journal of the American Planning Association, 61, 65, 10.1080/01944369508975620 Southworth, 2013 Sung, 2015, Residential built environment and walking activity: Empirical evidence of Jane Jacobs’ urban vitality, Transportation Research Part D: Transport and Environment, 41, 318, 10.1016/j.trd.2015.09.009 Tatem, 2017, WorldPop, open data for spatial demography, Scientific Data, 4, 170004, 10.1038/sdata.2017.4 Wang, 2020, Life between buildings from a street view image: What do big data analytics reveal about neighbourhood organisational vitality?, Urban Studies, 0042098020957198 WorldPop, 2018 Wu, 2021, Roofpedia: Automatic mapping of green and solar roofs for an open roofscape registry and evaluation of urban sustainability, Landscape and Urban Planning, 214, 104167, 10.1016/j.landurbplan.2021.104167 Wu, 2018, Check-in behaviour and spatio-temporal vibrancy: An exploratory analysis in Shenzhen, China, Cities, 77, 104, 10.1016/j.cities.2018.01.017 Wu, 2019, Influence of built environment on urban vitality: Case study of shanghai using mobile phone location data, Journal of Urban Planning and Development, 145, 10.1061/(ASCE)UP.1943-5444.0000513 Xia, 2020, Analyzing spatial relationships between urban land use intensity and urban vitality at street block level: A case study of five Chinese megacities, Landscape and Urban Planning, 193, 103669, 10.1016/j.landurbplan.2019.103669 Xiao, 2017, Identifying different transportation modes from trajectory data using tree-based ensemble classifiers, ISPRS International Journal of Geo-Information, 6, 57, 10.3390/ijgi6020057 Yang, 2021, Elaborating non-linear associations and synergies of subway access and land uses with urban vitality in Shenzhen, Transportation Research Part A: Policy and Practice, 144, 74 Ye, 2018, How block density and typology affect urban vitality: An exploratory analysis in Shenzhen, China, Urban Geography, 39, 631, 10.1080/02723638.2017.1381536 Ye, 2014, Quantitative tools in urban morphology: Combining space syntax, spacematrix and mixed-use index in a gis framework, Urban Morphology, 18, 97, 10.51347/jum.v18i2.3997 Yuan, 2019, Multilayer urban canopy modelling and mapping for traffic pollutant dispersion at high density urban areas, Science of the Total Environment, 647, 255, 10.1016/j.scitotenv.2018.07.409 Yue, 2019, Spatial explicit assessment of urban vitality using multi-source data: A case of Shanghai, China, Sustainability, 11, 638, 10.3390/su11030638 Yue, 2017, Measurements of POI-based mixed use and their relationships with neighbourhood vibrancy, International Journal of Geographical Information Science, 31, 658, 10.1080/13658816.2016.1220561 Zarin, 2015, Physical and social aspects of vitality case study: Traditional street and modern street in Tehran, Procedia-Social and Behavioral Sciences, 170, 659, 10.1016/j.sbspro.2015.01.068 Zeng, 2018, Spatially explicit assessment on urban vitality: Case studies in Chicago and Wuhan, Sustainable Cities and Society, 40, 296, 10.1016/j.scs.2018.04.021 Zhang, 2011, Mapping urbanization dynamics at regional and global scales using multi-temporal dmsp/ols nighttime light data, Remote Sensing of Environment, 115, 2320, 10.1016/j.rse.2011.04.032 Zhang, 2020, Graph deep learning model for network-based predictive hotspot mapping of sparse spatio-temporal events, Computers, Environment and Urban Systems, 79, 101403, 10.1016/j.compenvurbsys.2019.101403 Zhao, 2019, Applications of satellite remote sensing of nighttime light observations: Advances, challenges, and perspectives, Remote Sensing, 11, 1971, 10.3390/rs11171971 Zheng, 2017, Monitoring and assessing “ghost cities” in Northeast China from the view of nighttime light remote sensing data, Habitat International, 70, 34, 10.1016/j.habitatint.2017.10.005 Zhou, 2019, Social inequalities in neighborhood visual walkability: Using street view imagery and deep learning technologies to facilitate healthy city planning, Sustainable Cities and Society, 50, 101605, 10.1016/j.scs.2019.101605