The application of deep generative models in urban form generation based on topology: a review

References

• Abrantes, Patrícia, Jorge Rocha, Eduarda Marques da Costa, Eduardo Gomes, Paulo Morgado, and Nuno Costa. 2019. “Modelling Urban Form: A Multidimensional Typology of Urban Occupation for Spatial Analysis.” Environment and Planning B: Urban Analytics and City Science 46 (1): 47–65. doi:10.1177/2399808317700140
   Google Scholar
• Ackley, David H., Geoffrey E. Hinton, and Terrence J. Sejnowski. 1985. “A Learning Algorithm for Boltzmann Machines.” Cognitive Science 9 (1): 147–169. doi:10.1207/s15516709cog0901_7.
   Web of Science ®Google Scholar
• Adams, John S. 2005. “Hoyt, H. 1939: The Structure and Growth of Residential Neighborhoods in American Cities. Washington, DC: Federal Housing Administration. Washington, DC: Federal Housing Administration.” Progress in Human Geography 29 (3): 321–325. doi:10.1191/0309132505ph552xx.
   Web of Science ®Google Scholar
• Al-Sayed, Kinda. 2013. “Synthetic Space Syntax: A Generative and Supervised Learning Approach in Urban Design.” 2013 International space syntax symposium.
   Google Scholar
• Albert, Réka, and Albert László Barabási. 2002. “Statistical Mechanics of Complex Networks.” Reviews of Modern Physics 74 (1): 47–97. doi:10.1103/RevModPhys.74.47
   Web of Science ®Google Scholar
• Albert, Adrian, Emanuele Strano, Jasleen Kaur, and Marta González. 2018. “Modeling Urbanization Patterns with Generative Adversarial Networks.” In International Geoscience and Remote Sensing Symposium (IGARSS), edited by J. Morenoet al., 2095–2098. Valencia: Institute of Electrical and Electronics Engineers Inc.
   Google Scholar
• Anders Boesen Lindbo, Larsen, Søren Kaae Sønderby, Hugo Larochelle, and Ole Winther. 2016. Autoencoding beyond Pixels Using a Learned Similarity Metric.” In Proceedings of The 33rd International Conference on Machine Learning. Vol. 48, Proceedings of Machine Learning Research, edited by M. F. Balcan and K. Q. Weinberger, 1558-1566. New York: PMLR.
   Google Scholar
• As, Imdat, Siddharth Pal, and Prithwish Basu. 2018. “Artificial Intelligence in Architecture: Generating Conceptual Design via Deep Learning.” International Journal of Architectural Computing 16 (4): 306–327. doi:10.1177/1478077118800982.
   Web of Science ®Google Scholar
• Bacciu, Davide, Alessio Micheli, and Marco Podda. 2020. “Edge-Based Sequential Graph Generation with Recurrent Neural Networks.” Neurocomputing 416: 177–189. doi:10.1016/j.neucom.2019.11.112
   Web of Science ®Google Scholar
• Batty, Michael. 2007. Cities and Complexity: Understanding Cities with Cellular Automata, Agent-Based Models, and Fractals. Cambridge, Mass.; London: MIT.
   Google Scholar
• Batty, Michael. 2013. The New Science of Cities. Cambridge, Massachusetts: MIT Press.
   Google Scholar
• Batty, Michael, and James Cheshire. 2011. “Cities as Flows, Cities of Flows.” Environment and Planning B: Planning and Design 38 (2): 195–196. doi:10.1068/b3802ed
   Web of Science ®Google Scholar
• Beirão, José Nuno. 2012. “CItyMaker Designing Grammars for Urban Design.” Thesis, Delft University of Technology.
   Google Scholar
• Beirão, José Nuno, José Pinto Duarte, and Rudi Stouffs. 2011. “Creating Specific Grammars with Generic Grammars: Towards Flexible Urban Design.” Nexus Network Journal 13 (1): 73–111. doi:10.1007/s00004-011-0059-3.
   Web of Science ®Google Scholar
• Benedikt, M. L. 1979. “To Take Hold of Space: Isovists and Isovist Fields.” Environment and Planning B: Planning and Design 6 (1): 47–65. doi:10.1068/b060047
   Web of Science ®Google Scholar
• Bentley, Ian, and Georgia Butina. 1991. European Urban Design Exchanges.” Urban Design 38: 1–4.
   Google Scholar
• Bobkova, Evgeniya, Lars Marcus, Meta Berghauser Pont, Ioanna Stavroulaki, and David Bolin. 2019. “Structure of Plot Systems and Economic Activity in Cities: Linking Plot Types to Retail and Food Services in London, Amsterdam and Stockholm.” Urban Science 3 (3): 66. doi:10.3390/urbansci3030066
   Google Scholar
• Boeing, Geoff. 2018. “Measuring the Complexity of Urban Form and Design.” Urban Design International 23 (4): 281–292. doi:10.1057/s41289-018-0072-1.
   Web of Science ®Google Scholar
• Bojchevski, Aleksandar, Oleksandr Shchur, Daniel Zugner, and Stephan Gunnemann. 2018. “NetGAN: Generating Graphs via Random Walks.” 35th International conference on machine learning, ICML 2018 Pp. 973–988. Vol. 2.
   Google Scholar
• Borgwardt, Karsten, Elisabetta Ghisu, Felipe Llinares-López, Leslie O’Bray, and Bastian Rieck. 2020. "Graph Kernels: State-of-the-Art and Future Challenges." Foundations and Trends® in Machine Learning 13 (5–6): 531–712. doi:10.1561/2200000076.
   Google Scholar
• Campo, Matias Del, Alexandra Carlson, and Sandra Manninger. 2021. “Towards Hallucinating Machines - Designing with Computational Vision.” International Journal of Architectural Computing 19 (1): 88–103. doi:10.1177/1478077120963366.
   Web of Science ®Google Scholar
• Campo, Matias Del, Sandra Manninger, and Alexandra Carlson. 2020. “Hallucinating Cities - A Posthuman Design Method Based on Neural Networks.” In Proceedings of SimAUD, 255–262. San Diego: Society for Computer Simulation International.
   Google Scholar
• Cao, Nicola De, and Thomas Kipf. 2018. MolGAN: An Implicit Generative Model for Small Molecular Graphs.” ArXiv Preprint ArXiv: 1805.11973 1–11.
   Google Scholar
• Carmona, Matthew. 2021. Public Places Urban Spaces: The Dimensions of Urban Design. Third edit. New York: Routledge.
   Google Scholar
• Cataldi, G. 2003. “From Muratori to Caniggia: The Origins and Development of the Italian School of Design Typology.” Urban Morphology 7 (1): 19–34. doi:10.51347/jum.v7i1.3904.
   Web of Science ®Google Scholar
• Cataldi, Giancarlo, Gian Luigi Maffei, and Paolo Vaccaro. 2002. “Saverio Muratori and the Italian School of Planning Typology.” Urban Morphology 6 (1): 3–14. doi:10.51347/jum.v6i1.3899.
   Web of Science ®Google Scholar
• Chaillou, Stanislas. 2019. “AI + Architecture Towards a New Approach.” Thesis, Harvard University.
   Google Scholar
• Chen, Xi, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, and Pieter Abbeel. 2017. “Variational Lossy Autoencoder.” 5th International conference on learning representations, ICLR 2017 - conference track proceedings Pp. 1–17.
   Google Scholar
• Chu, Hang, Daiqing Li, David Acuna, Amlan Kar, Maria Shugrina, Xinkai Wei, Ming Yu Liu, Antonio Torralba, and Sanja Fidler. 2019. “Neural Turtle Graphics for Modeling City Road Layouts.” Proceedings of the IEEE international conference on computer vision Pp. 4521–4529.
   Google Scholar
• Clifton, Kelly, Reid Ewing, Gerrit Jan Knaap, and Yan Song. 2008. “Quantitative Analysis of Urban Form: A Multidisciplinary Review.” Journal of Urbanism 1 (1): 17–45. doi:10.1080/17549170801903496.
   Google Scholar
• Colaninno, Nicola, J. R. Cladera, and Karin Pfeffer. 2011. “An Automatic Classification of Urban Texture: Form and Compactness of Morphological Homogeneous Structures in Barcelona.” In New Challenges for European Regions and Urban Areas in a Globalised World, 1–15. Louvain-la-Neuve: European Regional Science Association (ERSA).
   Google Scholar
• Conzen, M. R. G. 1960. “Alnwick, Northumberland: A Study in Town-Plan Analysis.” Transactions and Papers (Institute of British Geographers) 27: iii–122. doi:10.1177/0309132509334948.
   Google Scholar
• Cowan, R. 2001. “Responding to the Challenge.” Planning 1413: 9.
   Google Scholar
• Creswell, Antonia, and Anil Anthony Bharath. 2016. “Adversarial Training for Sketch Retrieval.” In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), edited by G. Hua and H. Jégou, 798–809. Cham: Springer.
   Google Scholar
• Dempsey, Nicola, Caroline Brown, Shibu Raman, Sergio Porta, Mike Jenks, Bolin Jones, and Glen Bramley. 2009. “Elements of Urban Form.” In Dimensions of Sustainable City, edited by M. Jenks and C. Jones, 21–51. Dordrecht.: Springer.
   Google Scholar
• Dhariwal, Prafulla, and Alex Nichol. 2021. “Diffusion Models Beat GANs on Image Synthesis.” In Advances in Neural Information Processing Systems, edited by M. Ranzato, A. Beygelzimer, Y. Dauphin, P. S. Liang, and J. W. Vaughan, 8780–8794, Vol. 34. New York: Curran Associates, Inc.
   Google Scholar
• Dibble, Jacob. 2016. “Urban Morphometrics towards a Quantitative Science of Urban Form.” Thesis, University of Strathclyde.
   Google Scholar
• Dieleman, Sander. 2022. “Diffusion Models Are Autoencoders.” Retrieved April 7, 2022 (https://benanne.github.io/2022/01/31/diffusion.html).
   Google Scholar
• Dovey, K., E. Pafka, and M. Ristic. 2018. Mapping Urbanities: Morphologies, Flows, Possibilities. New York: Taylor & Francis Group.
   Google Scholar
• Dumoulin, Vincent, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, and Aaron Courville. 2017. “Adversarially Learned Inference.” ArXiv Preprint ArXiv:1606.00704 1–18.
   Google Scholar
• Erdös, P., and A. Rényi. 1960. “On the Evolution of Random Graphs.” Publication of the Mathematical Institute of the Hungarian Academy of Sciences 5 (1): 17–61. doi:10.1515/9781400841356.38.
   Google Scholar
• Euler, Leonhard. 1741. “Solutio Problematis Ad Geometriam Situs Pertinentis.” Commentarii Academiae Scientiarum Petropolitanae 8: 128–140.
   Google Scholar
• Fahlman, Scott E., Geoffrey E. Hinton, and Terrence J. Sejnowski. 1983. “Massively Parallel Architectures for Ai: Netl, Thistle, and Boltzmann Machines.” National conference on artificial intelligence, AAAI.
   Google Scholar
• Fan, Shuanfei, and Bert Huang. 2019. “Conditional Labeled Graph Generation with GANs.” In ICLR, 1–6.
   Google Scholar
• Fan, Shuangfei, Virginia Tech, and Bert Huang. 2019. Deep Generative Models for Labelled Graphs.” In ICLR Workshop DeepGenStruct, 1–10.
   Google Scholar
• Fischer, Asja, and Christian Igel. 2012. “An Introduction to Restricted Boltzmann Machines.” In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, edited by L. Alvarez, M. Mejail, L. Gomez, and J. Jacobo, 14–36. Berlin, Heidelberg: Springer Berlin Heidelberg.
   Google Scholar
• Fleischmann, Martin, Ombretta Romice, and Sergio Porta. 2020. “Measuring Urban Form: Overcoming Terminological Inconsistencies for a Quantitative and Comprehensive Morphologic Analysis of Cities.” Environment and Planning B: Urban Analytics and City Science 48 (8): 2133–2150. doi:10.1177/2399808320910444.
   Google Scholar
• Gauthier, Jon. 2014. “Conditional Generative Adversarial Nets for Convolutional Face Generation.” Class Project for Stanford CS231N: Convolutional Neural Networks for Visual Recognition, Winter Semester 2014 (5): 2.
   Google Scholar
• Gehl, Jan. 2011. Life Between Buildings: Using Public Space. Washington, DC: Island Press.
   Google Scholar
• Gil, Jorge, José Nuno Beirão, Nuno Montenegro, and José Pinto Duarte. 2012. “On the Discovery of Urban Typologies: Data Mining the Many Dimensions of Urban Form.” Urban Morphology 16 (1): 27–40. doi:10.51347/jum.v16i1.3966.
   Web of Science ®Google Scholar
• Glantz, Morton, and Johnathan Mun. 2011. “Projections and Risk Assessment.” In Credit Engineering for Bankers (Second Edition), edited by M. Glantz, and J. Mun, 185–236. Boston: Academic Press.
   Google Scholar
• Goodfellow, Ian J., Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2020. “Generative Adversarial Networks.” Communications of the ACM 63 (11): 139–144. doi:10.1145/3422622.
   Web of Science ®Google Scholar
• Goyal, Nikhil, Harsh Vardhan Jain, and Sayan Ranu. 2020. “GraphGen: A Scalable Approach to Domain-Agnostic Labeled Graph Generation.” Proceedings of the web conference 2020 Pp. 1253–1263.
   Google Scholar
• Grover, Aditya, Aaron Zweig, and Stefano Ermon. 2018. “Graphite: Iterative Generative Modeling of Graphs.” ArXiv Preprint ArXiv: 1803.10459 1–11.
   Google Scholar
• Gulrajani, Ishaan, Kundan Kumar, Faruk Ahmed, Adrien Ali Taiga, Francesco Visin, David Vazquez, and Aaron Courville. 2017. “Pixelvae: A Latent Variable Model for Natural Images.” 5th International conference on learning representations, ICLR 2017 - conference track proceedings Pp. 1-9.
   Google Scholar
• Guo, Xiaojie, and Liang Zhao. 2023. “A Systematic Survey on Deep Generative Models for Graph Generation.” IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (5): 5370–5390. doi:10.1109/TPAMI.2022.3214832.
   PubMed Web of Science ®Google Scholar
• Hamaina, Rachid, Thomas Leduc, Guillaume Moreau, Rachid Hamaina, Thomas Leduc, Guillaume Moreau, Towards Urban, and Fabrics Characterization. 2012. “Towards Urban Fabrics Characterization Based on Buildings Footprints.” In Bridging the Geographic Information Sciences, edited by Jérôme Gensel, Didier Josselin, and Danny Vandenbroucke, 327–346. Berlin, Heidelberg: Springer.
   Google Scholar
• Hartmann, Stefan, Michael Weinmann, Raoul Wessel, and Reinhard Klein. 2017. “StreetGAN: Towards Road Network Synthesis with Generative Adversarial Networks.” International conferences in central europe on computer graphics, visualization and computer vision Pp. 133–142, Vol. 2701.
   Google Scholar
• Hillier, Bill. 1996. Space Is the Machine: A Configurational Theory of Architecture. Cambridge: Cambridge University Press.
   Google Scholar
• Hillier, Bill. 2003. The Social Logic of Space. Cambridge, England: Cambridge University Press.
   Google Scholar
• Hillier, B., J. Hanson, and H. Graham. 1987. “Ideas Are in Things: An Application of the Space Syntax Method to Discovering House Genotypes.” Environment and Planning B: Planning and Design 14 (4): 363–385. doi:10.1068/b140363.
   Web of Science ®Google Scholar
• Hillier, B., A. Penn, J. Hanson, T. Grajewski, and J. Xu. 1993. “Natural Movement: Or, Configuration and Attraction in Urban Pedestrian Movement.” Environment and Planning B: Planning and Design 20 (1): 29–66. doi:10.1068/b200029.
   Web of Science ®Google Scholar
• Ho, Jonathan, Ajay Jain, and Pieter Abbeel. 2020. “Denoising Diffusion Probabilistic Models.” In Advances in Neural Information Processing Systems, 6840–6851. Vol. 33.
   Google Scholar
• Ho, Jonathan, Chitwan Saharia, William Chan, David J. Fleet, Mohammad Norouzi, and Tim Salimans. 2022. “Cascaded Diffusion Models for High Fidelity Image Generation.” Journal of Machine Learning Research 23 (1): 2249–2281. doi:10.5555/3586589.3586636.
   Google Scholar
• Hofmeister, Burkhard. 2022. “The Study of Urban Form in Germany.” Urban Morphology 8 (1): 3–12. doi:10.51347/jum.v8i1.3908.
   Google Scholar
• Hu, Zhiting, Zichao Yang, Ruslan Salakhutdinov, and Eric Xing. 2018. “On Unifying Deep Generative Models.” ArXiv Preprint ArXiv: 1706.00550 1–22.
   Google Scholar
• Huang, Jingnan, X. X. Lu, and Jefferey M. Sellers. 2007. “A Global Comparative Analysis of Urban Form: Applying Spatial Metrics and Remote Sensing.” Landscape and Urban Planning 82 (4): 184–197. doi:10.1016/j.landurbplan.2007.02.010
   Web of Science ®Google Scholar
• Huang, Weixin, and Hao Zheng. 2018. “Architectural Drawings Recognition and Generation through Machine Learning.” Proceedings of the 38th annual conference of the association for computer aided design in architecture, ACADIA 2018 Pp. 156–165.
   Google Scholar
• Hyndman, Rob J. 2018. Forecasting: Principles and Practice. Melbourne: OTexts.
   Google Scholar
• Jin, Wengong, Regina Barzilay, and Tommi Jaakkola. 2018. “Junction Tree Variational Autoencoder for Molecular Graph Generation.” International conference on machine learning Pp. 2323–2343.
   Google Scholar
• Karras, Tero, Samuli Laine, and Timo Aila. 2019. “A Style-Based Generator Architecture for Generative Adversarial Networks.” Proceedings of the IEEE computer society conference on computer vision and pattern recognition 2019-June Pp. 4396–4405.
   Google Scholar
• Kempinska, Kira, and Roberto Murcio. 2019. “Modelling Urban Networks Using Variational Autoencoders.” Applied Network Science 4 (114): 1–11. doi:10.1007/s41109-019-0234-0.
   Google Scholar
• Kingma, Diederik P., and Max Welling. 2014. “Auto-Encoding Variational Bayes.” 2nd International conference on learning representations, ICLR 2014 - conference track proceedings (Ml) 1-14.
   Google Scholar
• Koenig, Reinhard, Yufan Miao, Katja Knecht, Peter Buš, and Chang Mei-Chih. 2017. “Interactive Urban Synthesis: Computational Methods for Fast Prototyping of Urban Design Proposals.” In Communications in Computer and Information Science. Vol. 724, edited by Amin Mobasheri, Jane Li, Jorge Jr. Nakahara, and Amin Mobasheri, 23–41. Springer Verlag.
   Google Scholar
• Kostof, Spiro. 1999a. The City Assembled: The Elements of Urban Form Through History. 1st pbk. e. London: Thames and Hudson.
   Google Scholar
• Kostof, Spiro. 1999b. The City Shaped: Urban Patterns and Meanings Through History. London: Thames & Hudson.
   Google Scholar
• Kriege, Nils, and Petra Mutzel. 2012. “Subgraph Matching Kernels for Attributed Graphs.” Proceedings of the 29th international conference on machine learning, ICML 2012 Pp. 1015-1022, Vol. 2.
   Google Scholar
• Krier, L.1984. “Critiques and Urban Components.” In Houses, Palaces, Cities. Vol. 54, edited by Demetri Porphyrios, 231–250. London: Architectural Design.,
   Google Scholar
• Kropf, Karl. 2011. “Morphological Investigations: Cutting Into the Substance of Urban Form.” Built Environment 37 (4): 393–408. doi:10.2148/benv.37.4.393
   Google Scholar
• Kropf, Karl. 2017. The Handbook of Urban Morphology. 1st ed. Hoboken: Wiley.
   Google Scholar
• Kropf, Karl. 2022. “Aspects of Urban Form.” Urban Morphology 13 (2): 105–120. doi:10.51347/jum.v13i2.3949.
   Google Scholar
• Kropf, Karl. 2022. “Consolidating Urban Morphology as a Discipline K. Kropf.” Urban Morphology 18 (1): 70–72. doi:10.51347/jum.v18i1.4570.
   Google Scholar
• Lamb, Alex, Vincent Dumoulin, and Aaron Courville. 2016. “Discriminative Regularization for Generative Models.” ArXiv Preprint ArXiv: 1602.03220 1–10.
   Google Scholar
• Li, Yujia, Oriol Vinyals, Chris Dyer, Razvan Pascanu, and Peter Battaglia. 2018. “Learning Deep Generative Models of Graphs.” ArXiv Preprint ArXiv:1803.03324 1–21.
   Google Scholar
• Liao, Renjie, Yujia Li, Yang Song, Shenlong Wang, William L. Hamilton, David Duvenaud, Raquel Urtasun, and Richard Zemel. 2019. “Efficient Graph Generation with Graph Recurrent Attention Networks.” Proceedings of the 33rd international conference on neural information processing systems Pp. 1–13.
   Google Scholar
• Lilley, K. D. 2009. “Urban Morphology.” In International Encyclopedia of Human Geography, edited by Rob Kitchin and Nigel Thrift, 66–69. Elsevier.
   Google Scholar
• Lin, Bo, Wassim Jabi, and Rongdan Diao. 2020. “Urban Space Simulation Based on Wave Function Collapse and Convolutional Neural Network.” In SimAUD2020, 1–8. San Diego: Society for Computer Simulation International.
   Google Scholar
• Liu, Chia-Cheng, Harris Chan, Kevin Luk, and Borealis Ai. 2019. “Auto-Regressive Graph Generation Modeling with Improved Evaluation Methods.” In 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), 1–14. Vancouver.
   Google Scholar
• Long, Ying, Pai Li, and Jingxuan Hou. 2019. “Three-Dimensional Urban Form at the Street Block Level for Major Cities in China.” Shanghai Urban Planning Review 3: 10–15.
   Google Scholar
• Lowry, John H., and Michael B. Lowry. 2014. “Comparing Spatial Metrics That Quantify Urban Form.” Computers, Environment and Urban Systems 44: 59–67. doi:10.1016/j.compenvurbsys.2013.11.005.
   Web of Science ®Google Scholar
• Luca, Caneparo. 2007. “Generative Platform for Urban and Regional Design.” Automation in Construction 16 (1): 70–77. doi:10.1016/j.autcon.2005.10.008
   Web of Science ®Google Scholar
• Mercado, Rocío, Tobias Rastemo, Edvard Lindelöf, Günter Klambauer, Ola Engkvist, Hongming Chen, and Esben Jannik Bjerrum. 2020. “Practical Notes on Building Molecular Graph Generative Models.” Applied AI Letters 1 (2): 1–22. doi:10.1002/ail2.18.
   Google Scholar
• Miao, Yufan, Reinhard Koenig, Peter Bus, Mei-Chih Chang, Artem Chirkin, and Lukas Treyer. 2017a. “Empowering Urban Design Prototyping: A Case Study in Cape Town with Interactive Computational Synthesis Methods.” Protocols, flows and glitches, proceedings of the 22nd international conference of the association for computer-aided architectural design research in Asia (CAADRIA) Pp. 407-416.
   Google Scholar
• Miao, Yufan, Reinhard Koenig, and Katja Knecht. 2017b. “City Afforestation: Abstracting the Urban Geometries Into Tree Structures for Urban Fabric Optimization.” CUPUM: 15th International conference on computers in urban planning and urban management (1):2–9.
   Google Scholar
• Miao, Yufan, Reinhard Koenig, Katja Knecht, Kateryna Konieva, Peter Buš, and Mei-Chih Chang. 2018a. “Computational Urban Design Prototyping: Interactive Planning Synthesis Methods—a Case Study in Cape Town.” International Journal of Architectural Computing 16 (3): 212–226. doi:10.1177/1478077118798395
   Web of Science ®Google Scholar
• Miguel, Jaime de, Maria Eugenia Villafañe, Luka Piškorec, and Fernando Sancho-Caparrini. 2019. Deep Form Finding Using Variational Autoencoders for Deep Form Finding of Structural Typologies.” In Blucher Design Proceedings. Vol. 7, 71–80.
   Google Scholar
• Moundon, A. V. 2022. “Urban Morphology as an Emerging Interdisciplinary Field.” Urban Morphology 1: 3–10. doi:10.51347/jum.v1i1.4047.
   Google Scholar
• Nauata, Nelson, Kai-Hung Chang, Chin-Yi Cheng, Greg Mori, and Yasutaka Furukawa. 2020. House-GAN: Relational Generative Adversarial Networks for Graph-Constrained House Layout Generation.” Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I. Berlin: Springer-Verlag. 162–177. doi:10.1007/978-3-030-58452-8_10.
   Google Scholar
• Newton, David. 2019. “Generative Deep Learning in Architectural Design.” Technology Architecture and Design 3 (2): 176–189. doi:10.1080/24751448.2019.1640536.
   Google Scholar
• Nichol, Alex, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. 2021. “GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.” ArXiv Preprint ArXiv: 2112.10741 1–20.
   Google Scholar
• Nikolaev, Evgeny I. 2018. “Opportunities and Challenges in Deep Generative Models.” In CEUR Workshop Proceedings, 326–329.
   Google Scholar
• Oliveira, Vítor. 2016. Urban Morphology: An Introduction to the Study of the Physical Form of Cities. 2nd ed. Cham: Springer International Publishing.
   Google Scholar
• Oord, Aäron Van Den, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, and Koray Kavukcuoglu. 2016. “Conditional Image Generation with PixelCNN Decoders.” Proceedings of the 30th international conference on neural information processing systems, Curran Associates Inc Pp. 4797–4805.
   Google Scholar
• Orsini, Francesco, Paolo Frasconi, and Luc De Raedt. 2015. “Graph Invariant Kernels.” IJCAI International joint conference on artificial intelligence Pp. 3756–3762.
   Google Scholar
• Ou, Zhijian. 2018. “A Review of Learning with Deep Generative Models from Erspective of Graphical Modeling.” ArXiv Preprint ArXiv: 1808.01630 1–36.
   Google Scholar
• Oussidi, Achraf, and Azeddine Elhassouny. 2018. “Deep Generative Models: Survey.” 2018 International conference on intelligent systems and computer vision, ISCV 2018 Pp. 1-8.
   Google Scholar
• Owaki, Takashi, and Takashi Machida. 2020. “RoadNetGAN: Generating Road Networks in Planar Graph Representation.” In Communications in Computer and Information Science. Vol. 1332, edited by H. Yang, K. Pasupa, A. C. S. Leung, J. T. Kwok, J. H. Chan, and I. King, 535–543. Cham: Springer International Publishing. doi:10.1007/978-3-030-63820-7_61.
   Google Scholar
• Peaden, Stephen J. 2019. “Classification of Urban Forms and Their Relationship with Vegetation Cover in Cache County, Utah.” Thesis, Utah State University.
   Google Scholar
• Popova, Mariya, Mykhailo Shvets, Junier Oliva, and Olexandr Isayev. 2019. ”MolecularRNN: Generating Realistic Molecular Graphs with Optimized Properties.” ArXiv Preprint ArXiv: 1905.13372 1–10.
   Google Scholar
• Quan, Steven Jige. 2022. “Urban-GAN: An Artificial Intelligence-Aided Computation System for Plural Urban Design.” Environment and Planning B: Urban Analytics and City Science, doi:10.1177/23998083221100550.
   Google Scholar
• Radford, Alec, Luke Metz, and Soumith Chintala. 2016. “Unsupervised representation learning with deep convolutional generative adversarial networks.” 4th International conference on learning representations, ICLR 2016 - conference track proceedings Pp. 1-16.
   Google Scholar
• Rakha, Terek, and Christoph Reinhart. 2012. “Generative Urban Modeling: A Design Workflow for Walkability-Optimised Cities.” 5th National conference of IBPSA-USA Pp. 255-262.
   Google Scholar
• Rhee, Jinmo, and Ramesh Krishnamurti. 2020. “Integrating Building Footprint Prediction and Building Massing: An Experiment in Pittsburgh An Experiment in Pittsburgh.” 25th International conference of the association for computer aided arcthiectural design research in Asia (CAADRIA) Pp. 669–678.
   Google Scholar
• Rossi, Aldo. 1999. The Architecture of the City. Cambridge, Mass.; London: MIT.
   Google Scholar
• Ruthotto, Lars, and Eldad Haber. 2021. “An Introduction to Deep Generative Modeling.” GAMM Mitteilungen 44 (2): 1–26. doi:10.1002/gamm.202100008.
   Google Scholar
• Saharia, Chitwan, Jonathan Ho, William Chan, Tim Salimans, David J. Fleet, and Mohammad Norouzi. 2022. “Image Super-Resolution via Iterative Refinement.” IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (4): 4713–4726. doi:10.1109/TPAMI.2022.3204461.
   Web of Science ®Google Scholar
• Salakhutdinov, Ruslan. 2015. “Learning Deep Generative Models.” Annual Review of Statistics and Its Application 2: 361–385. doi:10.1146/annurev-statistics-010814-020120
   Web of Science ®Google Scholar
• Salakhutdinov, Ruslan, and Geoffrey Hinton. 2009. “Deep Boltzmann Machines.” In Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research. Vol. 5, edited by D. van Dyk and M. Welling, 448–455. Florida: PMLR.
   Google Scholar
• Samanta, Bidisha, Abir De, Gourhari Jana, Pratim Kumar Chattaraj, Niloy Ganguly, and Manuel Gomez Rodriguez. 2019. “NEVAE: A Deep Generative Model for Molecular Graphs.” 33rd AAAI Conference on artificial intelligence, AAAI 2019, 31st innovative applications of artificial intelligence conference, IAAI 2019 and the 9th AAAI symposium on educational advances in artificial intelligence, EAAI 2019 Pp.1110-1117.
   Google Scholar
• Samuel, A. L. 2000. “Some Studies in Machine Learning Using the Game of Checkers.” IBM Journal of Research and Development 44 (1.2): 206–226. doi:10.1147/rd.441.0206.
   Web of Science ®Google Scholar
• Scheer, Brenda Case. 2001. “The Anatomy of Sprawl.” Places 14 (2): 28–37.
   Google Scholar
• Schirmer, Patrick M., and Kay W. Axhausen. 2016. “A Multiscale Classification of Urban Morphology.” Journal of Transport and Land Use 9 (1): 101–130. doi:10.5198/jtlu.2015.667.
   Web of Science ®Google Scholar
• Schwarz, Nina. 2010. “Urban Form Revisited—Selecting Indicators for Characterising European Cities.” Landscape and Urban Planning 96 (1): 29–47. doi:10.1016/j.landurbplan.2010.01.007.
   Web of Science ®Google Scholar
• Seto, Karen C., Burak Güneralp, and Lucy R. Hutyra. 2012. “Global Forecasts of Urban Expansion to 2030 and Direct Impacts on Biodiversity and Carbon Pools.” Proceedings of the national academy of sciences of the united states of america Pp. 16083-16088. Vol. 109.
   Google Scholar
• Shen, Jiaqi, Chuan Liu, Y. U. E. Ren, and H. A. O. Zheng. 2020. “Machine Learning Assisted Urban Filling.” Proceedings of the 25th international conference on computer-aided architectural design research in Asia (CAADRIA), Bangkok, Thailand.
   Google Scholar
• Shi, Zhongming, Jimeno A. Fonseca, and Arno Schlueter. 2017. “A Review of Simulation-Based Urban Form Generation and Optimization for Energy-Driven Urban Design.” Building and Environment 121: 119–129. doi:10.1016/j.buildenv.2017.05.006.
   Web of Science ®Google Scholar
• Shi, Chence, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, and Jian Tang. 2020. “Graphaf: A Flow-Based Autoregressive Model for Molecular Graph Generation.” ArXiv Preprint ArXiv: 2001.09382 1-18.
   Google Scholar
• Simonovsky, Martin, and Nikos Komodakis. 2018. “GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11139 LNCS: 412–422.
   Google Scholar
• Slater, T. R. 1990. The Built Form of Western Cities: Essays for M.R.G. Conzen on the Occasion of His Eightieth Birthday. Leicester; London: Leicester University Press.
   Google Scholar
• Sohl-Dickstein, Jascha, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015. ““Deep Unsupervised Learning Using Nonequilibrium Thermodynamics.” 32nd International conference on machine learning, ICML 2015 Pp. 2246-2255. Vol. 3.
   Google Scholar
• Song, Yan. 2005. “Smart Growth and Urban Development Pattern: A Comparative Study.” International Regional Science Review 28 (2): 239–265. doi:10.1177/0160017604273854
   Web of Science ®Google Scholar
• Song, Yang, and Stefano Ermon. 2019. “Generative Modeling by Estimating Gradients of the Data Distribution.” Advances in Neural Information Processing Systems 32: 1–23.
   Google Scholar
• Song, Yan, and Gerrit Jan Knaap. 2004. “Measuring Urban Form: Is Portland Winning the War on Sprawl?” Journal of the American Planning Association 70 (2): 210–225. doi:10.1080/01944360408976371
   Web of Science ®Google Scholar
• Song, Jiaming, Chenlin Meng, and Stefano Ermon. 2020. “Denoising Diffusion Implicit Models.” ArXiv Preprint ArXiv: 2010.02502 1–22.
   Google Scholar
• Tensorflow. n.d. “Recurrent Neural Networks (RNN) with Keras.” Retrieved May 11, 2021 (https://www.tensorflow.org/guide/keras/rnn#introduction).
   Google Scholar
• Thirapongphaiboon, Taitawip, and Sean Hanna. 2019. “Spatial Distribution of Building Use: Recognition and Prediction of Use with Machine Learning.” 12th International space syntax symposium, SSS 2019 Pp. 1-30.
   Google Scholar
• Thompson, D’Arcy Wentworth. 1992. On Growth and Form. An Abridge. Cambridge: Cambridge University Press.
   Google Scholar
• Thünen, Johann Heinrich von. 1966. Von Thünen’s Isolated State: An English Edition of Der Isolierte Staat. Oxford: Pergamon.
   Google Scholar
• Turhan, Ceren Guzel, and Hasan Sakir Bilge. 2018. “Recent Trends in Deep Generative Models: A Review.” 3rd International conference on computer science and engineering (UBMK) Pp. 574-579.
   Google Scholar
• UCL Space Syntax. 2021. “Overview.” Retrieved July 5, 2021 (https://www.spacesyntax.online/overview-2/).
   Google Scholar
• United Nations. 2014. World Urbanization Prospects, the 2014 Revision. New York: Department of Economic and Social Affairs, Population Division.
   Google Scholar
• Uria, Benigno, Marc Alexandre Cote, Karol Gregor, Iain Murray, and Hugo Larochelle. 2016. “Neural Autoregressive Distribution Estimation.” Journal of Machine Learning Research 17 (1): 7184–7220. doi:10.5555/2946645.3053487.
   Google Scholar
• Wang, Ting Chun, Ming Yu Liu, Jun Yan Zhu, Andrew Tao, Jan Kautz, and Bryan Catanzaro. 2018. “High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs.” Proceedings of the IEEE computer society conference on computer vision and pattern recognition Pp.8798-8807.
   Google Scholar
• Wang, Hongwei, Jialin Wang, Jia Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Wenjie Li, Xing Xie, and Minyi Guo. 2021. “Learning Graph Representation with Generative Adversarial Nets.” IEEE Transactions on Knowledge and Data Engineering 33: 3090–3103. doi:10.1109/TKDE.2019.2961882.
   Web of Science ®Google Scholar
• Watts, Duncan J., and Steven H. Strogatz. 1998. “Collective Dynamics of ‘Small-World’ Networks.” Nature 393 (6684): 440–442. doi:10.1038/30918.
   PubMed Web of Science ®Google Scholar
• Weng, Lilian. 2021. “What Are Diffusion Models?” Retrieved April 8, 2020 (https://lilianweng.github.io/posts/2021-07-11-diffusion-models/).
   Google Scholar
• Whitehand, J. W. R. 2022. “British Urban Morphology: The Conzenian Tradition.” Urban Morphology 5 (2): 103–109. doi:10.51347/jum.v5i2.3896.
   Google Scholar
• Wu, Jiajun, Chengkai Zhang, Tianfan Xue, William T. Freeman, and Joshua B. Tenenbaum. 2016. “Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling.” Proceedings of the 30th international conference on neural information processing systems Pp.82-90.
   Google Scholar
• Xie, Zheng. 2011. “Exploration of the Potential Use of Space Syntax Analysis in 3D Parametric Design: The Case of Quanzhou.” Thesis, University College London.
   Google Scholar
• Xu, Jungang, Hui Li, and Shilong Zhou. 2015. “An Overview of Deep Generative Models.” IETE Technical Review 32 (2): 131–139. doi:10.1080/02564602.2014.987328
   Web of Science ®Google Scholar
• You, Jiaxuan, Bowen Liu, Rex Ying, Vijay Pande, and Jure Leskovec. 2018a. “Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation.” Proceedings of the 32nd international conference on neural information processing systems Pp. 6412–6422.
   Google Scholar
• You, Jiaxuan, Rex Ying, Xiang Ren, William L. Hamilton, and Jure Leskovec. 2018b. “GraphRNN: Generating Realistic Graphs with Deep Auto-Regressive Models.” 35th International conference on machine learning, ICML 2018 Pp. 9072-9081. Vol. 13.
   Google Scholar
• Zhang, Lei. 2010. “A Review on Urban Morphology Studies of The Western Countries And Its Enlightment.” Human Geography 25 (3): 90–95. doi:10.13959/j.issn.1003-2398.2010.03.003.
   Google Scholar
• Zhu, Jun Yan, Taesung Park, Phillip Isola, and Alexei A. Efros. 2017. “Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks.” Proceedings of the IEEE international conference on computer vision Pp. 2242–2251.
   Google Scholar