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Artificial Intelligence Based Methods for Smart and Sustainable Urban Planning: A Systematic Survey

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Abstract

The world’s cities are facing various challenges such as rapid urbanization, poverty, climate change, pollution, sustainable and inclusive development. Building futuristic smart and sustainable cities is proving to be a response to these challenges. Recent publications show that urban planning decision support is increasingly using artificial intelligence through machine learning methods to address these challenges. 172 papers were published in 2020 compared to 8 before 2010 with a forecast of at least 200 papers in 2021. Despite the explosive number of scientific publications on applications artificial intelligence for urban planning decision support, few studies have made a systematic assessment of the state of the art to inform future research. This would help focus on the approaches used, the planning problems most commonly addressed, the data used and even the study areas. We found that the top 5 most addressed urban planning issues include: land use/cover, urban growth, urban buildings, urban mobility and urban environment. Furthermore, a large amount of data was used from sensors and simple or ensemble machine learning methods were more used in this case. Deep learning methods are more used for land use/cover, buildings and climate issues which are mostly based on satellite image data. On the other hand, China and the United States are the most studied territories while Africa is almost not. A high intensity of collaboration between researchers affiliated with Chinese, American and English institutions was observed. Thus, urban planning researchers should benefit from this synthesis work by understanding the general idea of the application of machine learning methods in urban planning, its trends, issues, current challenges and future research directions.

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References

  1. Jordan Michael I, Mitchell Tom M (2015) Machine learning: trends, perspectives, and prospects. Science 349(6245):255–260

    Article  MathSciNet  MATH  Google Scholar 

  2. Tekouabou Stephane Cedric Koumetio, Diop El Bachir, Azmi Rida, Jaligot Rémi, Chenal Jérôme (2022) Reviewing the application of machine learning methods to model urban form indicators in planning decision support systems: potential, issues and challenges. J King Saud Univ-Comput Inf Sci 34(8, Part B):5943–5967

    Google Scholar 

  3. Bulkeley Harriet (2013) Cities and climate change. Routledge, London

    Book  Google Scholar 

  4. Koschwitz D, Frisch J, Van Treeck C (2018) Data-driven heating and cooling load predictions for non-residential buildings based on support vector machine regression and narx recurrent neural network: A comparative study on district scale. Energy 165:134–142

    Article  Google Scholar 

  5. Al-Garadi Mohammed Ali, Mohamed Amr, Al-Ali Abdulla Khalid, Xiaojiang Du, Ali Ihsan, Guizani Mohsen (2020) A survey of machine and deep learning methods for internet of things (iot) security. IEEE Commun Surv Tutor 22(3):1646–1685

    Article  Google Scholar 

  6. Li Xin, Cheng Shidan, Lv Zhihan, Song Houbing, Jia Tao, Ning Lu (2020) Data analytics of urban fabric metrics for smart cities. Future Gener Comput Syst 107:871–882

    Article  Google Scholar 

  7. Choung Yun-Jae, Kim Jin-Man (2019) Study of the relationship between urban expansion and pm10 concentration using multi-temporal spatial datasets and the machine learning technique: Case study for daegu, south korea. Appl Sci 9(6):1098

    Article  Google Scholar 

  8. Liu Lun, Silva Elisabete A, Chunyang Wu, Wang Hui (2017) A machine learning-based method for the large-scale evaluation of the qualities of the urban environment. Comput Environ Urban Syst 65:113–125

    Article  Google Scholar 

  9. Abrantes Patrícia, Rocha Jorge, Marques Eduarda, da Costa Eduardo, Gomes Paulo Morgado, Costa Nuno (2019) Modelling urban form: a multidimensional typology of urban occupation for spatial analysis. Environ Plan B 46(1):47–65

    Google Scholar 

  10. Machmud Roby Alhamidi and Wisnu Jatmiko (2020) Optimal feature aggregation and combination for two-dimensional ensemble feature selection. Information 11(1):38

    Article  Google Scholar 

  11. Schwarz Nina (2010) Urban form revisited-selecting indicators for characterising european cities. Landsc Urban Plan 96(1):29–47

    Article  Google Scholar 

  12. Gómez JA, Patino JE, Duque JC, Passos S (2020) Spatiotemporal modeling of urban growth using machine learning. Remote Sens 12(1):109

    Article  Google Scholar 

  13. Faghmous JH, Kumar V (2014) Spatio-temporal data mining for climate data: advances, challenges, and opportunities. Data mining and knowledge discovery for big data. Springer, New York, pp 83–116

    Chapter  MATH  Google Scholar 

  14. Jochem Warren C, Bird Tomas J, Tatem Andrew J (2018) Identifying residential neighbourhood types from settlement points in a machine learning approach. Comput Environ Urban Syst 69:104–113

    Article  Google Scholar 

  15. Ma Jun, Cheng Jack CP, Jiang Feifeng, Chen Weiwei, Zhang Jingcheng (2020) Analyzing driving factors of land values in urban scale based on big data and non-linear machine learning techniques. Land Use Policy 94:104537

    Article  Google Scholar 

  16. Geiß Christian, Schrade Henrik, Pelizari Patrick Aravena, Taubenböck Hannes (2020) Multistrategy ensemble regression for mapping of built-up density and height with sentinel-2 data. ISPRS J Photogramm Remote Sens 170:57–71

    Article  Google Scholar 

  17. Novack Tessio, Esch Thomas, Kux Hermann, Stilla Uwe (2011) Machine learning comparison between worldview-2 and quickbird-2-simulated imagery regarding object-based urban land cover classification. Remote Sens 3(10):2263–2282

    Article  Google Scholar 

  18. Hecht R, Herold H, Meinel G, Buchroithner M (2013) Automatic derivation of urban structure types from topographic maps by means of image analysis and machine learning. In: 26th international cartographic conference. pp 1–18

  19. Shafizadeh-Moghadam Hossein, Asghari Ali, Tayyebi Amin, Taleai Mohammad (2017) Coupling machine learning, tree-based and statistical models with cellular automata to simulate urban growth. Comput Environ Urban Syst 64:297–308

    Article  Google Scholar 

  20. Lee Changyeon (2019) Impacts of urban form on air quality: emissions on the road and concentrations in the us metropolitan areas. J Environ Manage 246:192–202

    Article  Google Scholar 

  21. Milojevic-Dupont N, Hans N, Kaack LH, Zumwald M, Andrieux F, de Barros Soares D, Lohrey S, Pichler P-P, Creutzig F (2020) Learning from urban form to predict building heights. PLoS ONE 15(12):e0242010

    Article  Google Scholar 

  22. Okwuashi O, Ndehedehe CE (2020) Integrating machine learning with Markov chain and cellular automata models for modelling urban land use change. Remote Sens Appl: Soc Environ 21:100461

    Google Scholar 

  23. Huang Bo, Zhao Bei, Song Yimeng (2018) Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery. Remote Sens Environ 214:73–86

    Article  Google Scholar 

  24. Hagenauer Julian, Helbich Marco (2012) Mining urban land-use patterns from volunteered geographic information by means of genetic algorithms and artificial neural networks. Int J Geogr Inf Sci 26(6):963–982

    Article  Google Scholar 

  25. Geng X, Li Y, Wang L, Zhang L, Yang Q, Ye J, Liu Y (2019) Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, AAAI Press, Palo Alto, pp 3656–3663

  26. He Qingsong, He Weishan, Song Yan, Jiayu Wu, Yin Chaohui, Mou Yanchuan (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 78:726–738

    Article  Google Scholar 

  27. Gao Sihang, Zhan Qingming, Yang Chen, Liu Huimin (2020) The diversified impacts of urban morphology on land surface temperature among urban functional zones. Int J Environ Res Public Health 17(24):9578

    Article  Google Scholar 

  28. Reades Jonathan, De Souza Jordan, Hubbard Phil (2019) Understanding urban gentrification through machine learning. Urban Stud 56(5):922–942

    Article  Google Scholar 

  29. Lin Trista, Rivano Hervé, Le Mouël Frédéric (2017) A survey of smart parking solutions. IEEE Trans Intell Transp Syst 18(12):3229–3253

    Article  Google Scholar 

  30. Mitchell TM et al (1997) Machine learning, Vol 1, no 9. McGraw-hill, New York

  31. Munoz A (2014) Machine learning and optimization. https://www.cims.nyu.edu/munoz/files/ml_optimization.pdf. Accessed 2 Mar 2016

  32. Mohri M, Rostamizadeh A, Talwalkar A (2018) Foundations of machine learning. MIT press, Cambridge

    MATH  Google Scholar 

  33. Rish I et al (2001) An empirical study of the naive bayes classifier. In: IJCAI 2001 workshop on empirical methods in artificial intelligence, vol 3, pp 41–46

  34. Borchmann Daniel, Hanika Tom, Obiedkov Sergei (2020) Probably approximately correct learning of horn envelopes from queries. Discrete Appl Math 273:30–42

    Article  MathSciNet  MATH  Google Scholar 

  35. Kleine Deters J, Zalakeviciute R, Gonzalez M, Rybarczyk Y (2017) Modeling PM2.5 urban pollution using machine learning and selected meteorological parameters. J Electr Comput Eng. https://doi.org/10.1155/2017/5106045

    Article  Google Scholar 

  36. Xayasouk Thanongsak, Lee HwaMin, Lee Giyeol (2020) Air pollution prediction using long short-term memory (lstm) and deep autoencoder (dae) models. Sustainability 12(6):2570

    Article  Google Scholar 

  37. Janarthanan R, Partheeban P, Somasundaram K, Elamparithi PN (2021) A deep learning approach for prediction of air quality index in a metropolitan city. Sustain Cities Soc 67:102720

    Article  Google Scholar 

  38. Chan Jonathan Cheung-Wai, Chan Kwok-Ping, Yeh Anthony Gar-On (2001) Detecting the nature of change in an urban environment: a comparison of machine learning algorithms. Photogramm Eng Remote Sens 67(2):213–226

    Google Scholar 

  39. Kontokosta Constantine E, Hong Boyeong, Johnson Nicholas E, Starobin Daniel (2018) Using machine learning and small area estimation to predict building-level municipal solid waste generation in cities. Comput Environ Urban Syst 70:151–162

    Article  Google Scholar 

  40. Zhongqi Yu, Chen Shisheng, Wong Nyuk Hien, Ignatius Marcel, Deng Jiyu, He Yueer, Hii Daniel Jun Chung (2020) Dependence between urban morphology and outdoor air temperature: a tropical campus study using random forests algorithm. Sustain Cities Soc 61:102200

    Article  Google Scholar 

  41. Chen Shisheng, Zhang Wen, Wong Nyuk Hien, Ignatius Marcel (2020) Combining citygml files and data-driven models for microclimate simulations in a tropical city. Build Environ 185:107314

    Article  Google Scholar 

  42. Sun Yanwei, Gao Chao, Li Jialin, Wang Run, Liu Jian (2019) Quantifying the effects of urban form on land surface temperature in subtropical high-density urban areas using machine learning. Remote Sens 11(8):959

    Article  Google Scholar 

  43. Middel Ariane, Lukasczyk Jonas, Maciejewski Ross, Demuzere Matthias, Roth Matthias (2018) Sky view factor footprints for urban climate modeling. Urban Climate 25:120–134

    Article  Google Scholar 

  44. Chen Yimin, Li Xia, Wang Shujie, Liu Xiaoping, Ai Bin (2013) Simulating urban form and energy consumption in the pearl river delta under different development strategies. Ann Assoc Am Geogr 103(6):1567–1585

    Article  Google Scholar 

  45. Robinson Caleb, Dilkina Bistra, Hubbs Jeffrey, Zhang Wenwen, Guhathakurta Subhrajit, Brown Marilyn A, Pendyala Ram M (2017) Machine learning approaches for estimating commercial building energy consumption. Appl Energy 208:889–904

    Article  Google Scholar 

  46. Han Mengjie, Wang Zhenwu, Zhang Xingxing (2021) An approach to data acquisition for urban building energy modeling using a gaussian mixture model and expectation-maximization algorithm. Buildings 11(1):30

    Article  Google Scholar 

  47. Duerr Isaac, Merrill Hunter R, Wang Chuan, Bai Ray, Boyer Mackenzie, Dukes Michael D, Bliznyuk Nikolay (2018) Forecasting urban household water demand with statistical and machine learning methods using large space-time data: A comparative study. Environ Model Softw 102:29–38

    Article  Google Scholar 

  48. Chang Soowon, Saha Nirvik, Castro-Lacouture Daniel, Yang Perry Pei-Ju (2019) Generative design and performance modeling for relationships between urban built forms, sky opening, solar radiation and energy. Energy Procedia 158:3994–4002

    Article  Google Scholar 

  49. Liu Zhiyuan, Liu Yang, Meng Qiang, Cheng Qixiu (2019) A tailored machine learning approach for urban transport network flow estimation. Transp Res C 108:130–150

    Article  Google Scholar 

  50. Saiqun Lu, Zhang Qiyan, Chen Guangsen, Seng Dewen (2021) A combined method for short-term traffic flow prediction based on recurrent neural network. Alex Eng J 60(1):87–94

    Article  Google Scholar 

  51. Fu R, Zhang Z, Li L (2016) Using lstm and gru neural network methods for traffic flow prediction. In: 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), IEEE, pp 324–328

  52. Jack Elizabeth, McCormack Gavin R (2014) The associations between objectively-determined and self-reported urban form characteristics and neighborhood-based walking in adults. Int J Behav Nutr Phys Act 11(1):71

    Article  Google Scholar 

  53. Yandan Lu, Kawamura Kazuya, Zellner Moira L (2008) Exploring the influence of urban form on work travel behavior with agent-based modeling. Transp Res Rec 2082(1):132–140

    Article  Google Scholar 

  54. Middel Ariane, Lukasczyk Jonas, Zakrzewski Sophie, Arnold Michael, Maciejewski Ross (2019) Urban form and composition of street canyons: a human-centric big data and deep learning approach. Landsc Urban Plan 183:122–132

    Article  Google Scholar 

  55. Arribas-Bel D, Garcia-López MÀ, Viladecans-Marsal E (2019) Building (s and) cities: delineating urban areas with a machine learning algorithm. J Urban Econ 125:103217

    Article  Google Scholar 

  56. Kafy AA, Naim MN, Subramanyam G, Ahmed NU, Al Rakib A, Kona MA, Sattar GS (2021) Cellular automata approach in dynamic modeling of land cover changes using rapideye images in Dhaka, Bangladesh. Environ Chall 4:100084

    Article  Google Scholar 

  57. Schneider Annemarie (2012) Monitoring land cover change in urban and peri-urban areas using dense time stacks of landsat satellite data and a data mining approach. Remote Sens Environ 124:689–704

    Article  Google Scholar 

  58. Nice Kerry A, Thompson Jason, Wijnands Jasper S, Aschwanden Gideon DPA, Stevenson Mark (2020) The “Paris-end’’ of town? Deriving urban typologies using three imagery types. Urban Sci 4(2):27

    Article  Google Scholar 

  59. Moosavi V (2017) Urban morphology meets deep learning: exploring urban forms in one million cities, town and villages across the planet. arXiv preprint arXiv:1709.02939,

  60. Vakalopoulou M, Karantzalos K, Komodakis N, Paragios N (2015) Building detection in very high resolution multispectral data with deep learning features. In: 2015 IEEE international geoscience and remote sensing symposium (IGARSS), IEEE, pp 1873–1876

  61. Persello Claudio, Stein Alfred (2017) Deep fully convolutional networks for the detection of informal settlements in vhr images. IEEE Geosci Remote Sens Lett 14(12):2325–2329

    Article  Google Scholar 

  62. Verma Deepank, Jana Arnab, Ramamritham Krithi (2019) Transfer learning approach to map urban slums using high and medium resolution satellite imagery. Habitat Int 88:101981

    Article  Google Scholar 

  63. Xing Hanfa, Meng Yuan (2020) Measuring urban landscapes for urban function classification using spatial metrics. Ecol Indic 108:105722

    Article  Google Scholar 

  64. Noulas A, Mascolo C, Frias-Martinez E (2013) Exploiting foursquare and cellular data to infer user activity in urban environments. In: 2013 IEEE 14th international conference on mobile data management, vol 1, IEEE, , pp 167–176

  65. Dempsey Nicola, Brown Caroline, Raman Shibu, Porta Sergio, Jenks Mike, Jones Colin, Bramley Glen (2010) Elements of urban form. Dimensions of the sustainable city. Springer, Dordrecht, pp 21–51

    Google Scholar 

  66. Huang Jingnan, Lu Xi X, Sellers Jefferey M (2007) A global comparative analysis of urban form: applying spatial metrics and remote sensing. Landsc Urban Plan 82(4):184–197

    Article  Google Scholar 

  67. Frenkel Amnon, Ashkenazi Maya (2008) Measuring urban sprawl: how can we deal with it? Environ Plan B 35(1):56–79

    Article  Google Scholar 

  68. Chaudhary Khyati, Yadav Jyoti, Mallick Bhawna (2012) A review of fraud detection techniques: credit card. Int J Comput Appl 45(1):39–44

    Google Scholar 

  69. Budgen D, Brereton P (2006) Performing systematic literature reviews in software engineering. In: Proceedings of the 28th international conference on Software engineering, pp 1051–1052

  70. Marew Tegegne, Kim Jungyoon, Bae Doo Hwan (2007) Systematic functional decomposition in a product line using aspect-oriented software development: a case study. Int J Softw Eng Knowl Eng 17(01):33–55

    Article  Google Scholar 

  71. Petersen Kai, Vakkalanka Sairam, Kuzniarz Ludwik (2015) Guidelines for conducting systematic mapping studies in software engineering: an update. Inf Softw Technol 64:1–18

    Article  Google Scholar 

  72. Falagas Matthew E, Pitsouni Eleni I, Malietzis George A, Pappas Georgios (2008) Comparison of pubmed, scopus, web of science, and google scholar: strengths and weaknesses. FASEB J 22(2):338–342

    Article  Google Scholar 

  73. Demšar J, Zupan B, Leban G, Curk T (2004) Orange: from experimental machine learning to interactive data mining. European conference on principles of data mining and knowledge discovery. Springer, Berlin, pp 537–539

    Google Scholar 

  74. Demšar J, Curk T, Erjavec A, Č Gorup, Hočevar T, Milutinovič M, Možina M, Polajnar M, Toplak M, Starič A et al (2013) Orange: data mining toolbox in python. J Mach Learn Res 14(1):2349–2353

    MATH  Google Scholar 

  75. Niu Haifeng, Silva Elisabete A (2020) Crowdsourced data mining for urban activity: review of data sources, applications, and methods. J Urban Plan Dev 146(2):04020007

    Article  Google Scholar 

  76. Zhang Fan, Zhou Bolei, Liu Liu Yu, Liu Helene H, Fung Hui Lin, Ratti Carlo (2018) Measuring human perceptions of a large-scale urban region using machine learning. Landsc Urban Plan 180:148–160

    Article  Google Scholar 

  77. Xianyuan Zhan Yu, Zheng Xiuwen Yi, Ukkusuri Satish V (2016) Citywide traffic volume estimation using trajectory data. IEEE Trans Knowl Data Eng 29(2):272–285

    Article  Google Scholar 

  78. Biljecki Filip, Ledoux Hugo, Stoter Jantien (2017) Generating 3d city models without elevation data. Comput Environ Urban Syst 64:1–18

    Article  Google Scholar 

  79. Zhenyu Lu, Im Jungho, Rhee Jinyoung, Hodgson Michael (2014) Building type classification using spatial and landscape attributes derived from lidar remote sensing data. Landsc Urban Plan 130:134–148

    Article  Google Scholar 

  80. Chen Yang, Fan Rongshuang, Yang Xiucheng, Wang Jingxue, Latif Aamir (2018) Extraction of urban water bodies from high-resolution remote-sensing imagery using deep learning. Water 10(5):585

    Article  Google Scholar 

  81. Lv Q, Dou Y, Niu X, Xu J, Xu J, Xia F (2015) Urban land use and land cover classification using remotely sensed sar data through deep belief networks. J Sens. https://doi.org/10.1155/2015/538063

    Article  Google Scholar 

  82. Nutkiewicz Alex, Yang Zheng, Jain Rishee K (2018) Data-driven urban energy simulation (due-s): a framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow. Appl Energy 225:1176–1189

    Article  Google Scholar 

  83. Zhang Hongsheng, Ru Xu (2018) Exploring the optimal integration levels between sar and optical data for better urban land cover mapping in the pearl river delta. Int J Appl Earth Obs Geoinf 64:87–95

    Google Scholar 

  84. Huang Xin, Xie Cong, Fang Xing, Zhang Liangpei (2015) Combining pixel-and object-based machine learning for identification of water-body types from urban high-resolution remote-sensing imagery. IEEE J Sel Top Appl Earth Obs Remote Sens 8(5):2097–2110

    Article  Google Scholar 

  85. Yoo Cheolhee, Im Jungho, Park Seonyoung, Quackenbush Lindi J (2018) Estimation of daily maximum and minimum air temperatures in urban landscapes using modis time series satellite data. ISPRS J Photogramm Remote Sens 137:149–162

    Article  Google Scholar 

  86. Huang Bo, Xie Chenglin, Tay Richard, Bo Wu (2009) Land-use-change modeling using unbalanced support-vector machines. Environ Plan B 36(3):398–416

    Article  Google Scholar 

  87. Aurelia Magdalena Pisoschi and Claudia Gabriela Pisoschi (2016) Is open access the solution to increase the impact of scientific journals? Scientometrics 109(2):1075–1095

    Article  Google Scholar 

  88. Van Eck NJ, Waltman L (2013) Vosviewer manual. Leiden: Univeristeit Leiden 1(1):1–53

    Google Scholar 

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  1. Center of Urban Systems (CUS), Mohammed VI Polytechnic University (UM6P), Hay Moulay Rachid, 43150, Ben Guerir, Morocco

    Stéphane Cédric Koumetio Tekouabou, El Bachir Diop, Rida Azmi & Jérôme Chenal

  2. Urban and Regional Planning Community (CEAT), Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland

    Jérôme Chenal

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  1. Stéphane Cédric Koumetio Tekouabou
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Koumetio Tekouabou, S.C., Diop, E.B., Azmi, R. et al. Artificial Intelligence Based Methods for Smart and Sustainable Urban Planning: A Systematic Survey. Arch Computat Methods Eng 30, 1421–1438 (2023). https://doi.org/10.1007/s11831-022-09844-2

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