This study proposes a prototype for the smart rendering of urban master plans via artificial intelligence algorithms, a process which is time-consuming and relies on professionals’ experience. With the help of crowdsourced data and generative adversarial networks (GAN), a generation model was trained to provide colorful rendering of master plans similar to those produced by experienced urban designers. Approximately 5000 master plans from Pinterest were processed and CycleGAN was applied as the core algorithm to build this model, the so-called MasterplanGAN. Using the uncolored input design files in an AutoCAD format, the MasterplanGAN can provide master plan renderings within a few seconds. The validation of the generated results was achieved using quantitative and qualitative judgments. The achievements of this study contribute to the development of automatic generation of previously subjective and experience-oriented processes, which can serve as a useful tool for urban designers and planners to save time in real projects. It also contributes to push the methodological boundaries of urban design by addressing urban design requirements with new urban data and new techniques. This initial exploration indicates that a large but clear picture of computational urban design can be presented, integrating scientific thinking, design, and computer techniques.
ÇalışkanO (2017) Parametric design in urbanism: A critical reflection. Planning Practice & Research32(4): 417–443.
2.
CannyJ (1986) A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence (6): 679–698.
3.
CantrellBMichaelsW (2015) Digital Drawing for Landscape Architecture: Contemporary Techniques and Tools for Digital Representation in Site Design. New York: John Wiley & Sons.
4.
ChaillouS (2019) AI+ Architecture: Towards a New Approach. Cambridge: Harvard University.
D'AcciL (ed.) (2019) The Mathematics of Urban Morphology. New York: Springer International Publishing.
7.
DevlinJChangMWLeeK, et al. (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
8.
FrederickMMehtaV (2018) 101 Things I Learned in Urban Design School. New York: Crown.
9.
GoodfellowIPouget-AbadieJMirzaM, et al. (2014) Generative adversarial nets. Advances in Neural Information Processing Systems27: 2672–2680.
10.
HanYSYooJYeJC (2016) Deep residual learning for compressed sensing CT reconstruction via persistent homology analysis. arXiv preprint arXiv:1611.06391.
11.
HeW (2020) Urban experiment: Taking off on the wind of Al. Architectural Design90(3): 94–99.
12.
HeWYangX (2019) Artificial intelligence design, from research to practice. In: The International Conference on Computational Design and Robotic Fabrication, Shanghai, China, 7–8 July 2019, pp.189–198. Singapore: Springer.
13.
HeuselMRamsauerHUnterthinerT, et al. (2017) GANs trained by a two time-scale update rule converge to a local Nash equilibrium. Advances in Neural Information Processing Systems30: 6626–6637.
14.
HuCBZhangFGongFY, et al. (2020) Classification and mapping of urban canyon geometry using Google Street View images and deep multitask learning. Building and Environment167: 106424.
15.
HuangWZhengH (2018) Architectural drawings recognition and generation through machine learning. In: Proceedings of the 38th association for computer aided design in architecture conference 2017, Mexico City, Mexico, 18–20 October, pp. 156–165. Cambridge, MA: Acadia Publishing Company.
16.
IsolaPZhuJYZhouT, et al. (2017) Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA, 21–26 July 2017, pp.1125–1134. Washington, DC: IEEE Computer Society.
17.
JingYLiuDKislyukD, et al. (2015) Visual search at Pinterest. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, Sydney, NSW, Australia, August 2015, pp.1889–1898. New York: ACM.
18.
JonesJC (1992) Design Methods. New York: John Wiley & Sons.
19.
KangYGaoSRothRE (2019) Transferring multiscale map styles using generative adversarial networks. International Journal of Cartography5(2–3): 115–141.
20.
KarimiK (2012) A configurational approach to analytical urban design: ‘Space syntax’ methodology. Urban Design International17(4): 297–318.
21.
KarrasTLaineSAilaT (2019) A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Long Beach, CA, USA, 15–21 June 2019, pp.4401–4410. Piscataway: IEEE.
22.
KarrasTLaineSAittalaM, et al. (2020) Analyzing and improving the image quality of StyleGAN. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Virtual, Online, 14–19 June 2020, pp.8110–8119. Piscataway: IEEE.
23.
KeysR (1981) Cubic convolution interpolation for digital image processing. IEEE Transactions on Acoustics, Speech, and Signal Processing29(6): 1153–1160.
24.
KoenigRMiaoYAichingerA, et al. (2020) Integrating urban analysis, generative design, and evolutionary optimization for solving urban design problems. Environment and Planning B: Urban Analytics and City Science47(6): 997–1013.
25.
KvanT (2020) Data-informed design: A call for theory. Architectural Design90(3): 26–31.
26.
LangJ (2005) Urban Design: A Typology of Procedures and Products. Amsterdam: Routledge.
27.
LawsonB (2005) How Designers Think: The Design Process Demystified. London: Architectural Press.
28.
LevenshteinVI (1966) Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady10(8): 707–710.
29.
LiXZhangCLiW, et al. (2015) Who lives in greener neighborhoods? The distribution of street greenery and its association with residents’ socioeconomic conditions in Hartford, Connecticut, USA. Urban Forestry & Urban Greening14(4): 751–759.
30.
LiuLZhangHJiY, et al. (2019) Toward AI fashion design: An Attribute-GAN model for clothing match. Neurocomputing341: 156–167.
31.
LongYHuangCC (2019) Does block size matter? The impact of urban design on economic vitality for Chinese cities. Environment and Planning B: Urban Analytics and City Science46(3): 406–422.
32.
LongYYeY (2019) Measuring human-scale urban form and its performance. Landscape and Urban Planning191: 103612.
33.
MiaoYKoenigRKnechtK, et al. (2018) Computational urban design prototyping: Interactive planning synthesis methods—A case study in Cape Town. International Journal of Architectural Computing16(3): 212–226.
34.
MoughtinCCuestaRSarrisC, et al. (1999) Urban Design: Method and Technique. London: Architectural Press.
35.
NaikNPhilipoomJRaskarR, et al. (2014) Streetscore-predicting the perceived safety of one million streetscapes. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, Columbus, OH, USA, 24–27 June, pp.779–785. Piscataway: IEEE.
36.
NauataNChangKHChengCY, et al. (2020) House-GAN: Relational generative adversarial networks for graph-constrained house layout generation. arXiv preprint arXiv:2003.06988.
37.
NoymanADoorleyRXiongZ, et al. (2019) Reversed urbanism: Inferring urban performance through behavioral patterns in temporal telecom data. Environment and Planning B: Urban Analytics and City Science46(8): 1480–1498.
38.
OffenhuberDRattiC (2014) Decoding the City: Urbanism in the Age of Big Data. Basel: Birkhäuser.
39.
OliveiraVMedeirosV (2016) Morpho: Combining morphological measures. Environment and Planning B: Planning and Design43(5): 805–825.
40.
RoeJJAspinallPAMavrosP, et al. (2013) Engaging the brain: The impact of natural versus urban scenes using novel EEG methods in an experimental setting. Environmental Sciences1(2): 93–104.
41.
SchützeHManningCDRaghavanP (2008) Introduction to Information Retrieval. Cambridge: Cambridge University Press.
42.
SugiharaS (2019) Design outside of the frame: A role of architects in the era of artificial intelligence. In: Instabilities and Potentialities. London: Routledge, pp.176–182.
43.
SzegedyCVanhouckeVIoffeS, et al. (2016) Rethinking the inception architecture for computer vision. Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA, 27–30 June 2016, pp.2818–2826. Washington, DC: IEEE Computer Society.
44.
TangZYeYJiangZ, et al. (2020) A data-informed analytical approach to human-scale greenway planning: Integrating multi-sourced urban data with machine learning algorithms. Urban Forestry & Urban Greening56: 126871.
45.
TownsendA (2015) Cities of data: Examining the new urban science. Public Culture27(2(76)): 201–212.
TuringAM (1950) Mind-a quarterly review of psychology and philosophy. Computing Machinery and Intelligence59(236): 433–460.
48.
UlyanovDVedaldiALempitskyV (2016) Instance normalization: The missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022.
49.
WilsonLDanforthJDavilaCC, et al. (2019) How to generate a thousand master plans: A framework for computational urban design. In: Proceedings of the symposium on simulation for architecture and urban design (SimAUD), Atlanta, GA, USA, 7–9 April, pp.113–120. San Diego, CA: The Society for Modeling and Simulation International.
50.
XuBWangNChenT, et al. (2015) Empirical evaluation of rectified activations in convolutional network. http://arxiv.org/abs/1505.00853
51.
YangSWangZWangZ, et al. (2019) Controllable artistic text style transfer via shape-matching GAN. In: Proceedings of the IEEE international conference on computer vision, Seoul, South Korea, 27 October–3 November 2019, pp.4442–4451. Piscataway: IEEE.
52.
YeYHuangRZhangL (2020) Quantitative urban morphology: Conceptualization and urban design’s response. Time + Architecture2021(1): 34–43 (in Chinese).
53.
YeYLiDLiuX (2018) How block density and typology affect urban vitality: An exploratory analysis in Shenzhen, China. Urban Geography39(4): 631–652.
54.
YeYVan NesA (2014) Quantitative tools in urban morphology: Combining space syntax, spacematrix and mixed-use index in a GIS framework. Urban Morphology18(2): 97–118.
55.
Ye Y, Yeh A, Zhuang Y, et al. (2017) “Form Syntax” as a contribution to geodesign: A morphological tool for urbanity-making in urban design. Urban Design International 22(1): 73–90.
56.
YeYZengWShenQ, et al. (2019) The visual quality of streets: A human-centred continuous measurement based on machine learning algorithms and street view images. Environment and Planning B: Urban Analytics and City Science46(8): 1439–1457.
57.
YuYGongZZhongP, et al. (2017) Unsupervised representation learning with deep convolutional neural network for remote sensing images. International Conference on Image and Graphics, Shanghai, China, 13–15 September 2017, pp.97–108. Cham, Switzerland: Springer.
58.
ZhangALiWWuJ, et al. (2020) How can the urban landscape affect urban vitality at the street block level? A case study of 15 metropolises in China. Environment and Planning B: Urban Analytics and City Science. Epub ahead of print 18 May 2020. DOI: 10.1177/2399808320924425.
59.
ZhangYDongR (2018) Impacts of street-visible greenery on housing prices: Evidence from a hedonic price model and a massive street view image dataset in Beijing. ISPRS International Journal of Geo-Information7(3): 104.
60.
ZhuJYParkTIsolaP, et al. (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision, Venice, Italy, 22–29 October 2017, pp.2223–2232. Washington, DC: IEEE Computer Society.
