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