Skip to main content

Advertisement

Springer Nature Link
Log in

Automated urban planning aware spatial hierarchies and human instructions

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

Traditional urban planning demands urban experts to spend much time producing an optimal urban plan under many architectural constraints. The remarkable imaginative ability of deep generative learning provides hope for renovating this domain. Existing works are constrained by: (1) neglecting human requirements; (2) omitting spatial hierarchies, and (3) lacking urban plan samples. We propose a novel, deep human-instructed urban planner to fill these gaps and implement two practical frameworks. In the preliminary version, we formulate the task into an encoder–decoder paradigm. The encoder is to learn the information distribution of surrounding contexts, human instructions, and land-use configuration. The decoder is to reconstruct the land-use configuration and the associated urban functional zones. Although it has achieved good results, the generation performance is still unstable due to the complex optimization directions of the decoder. Thus, we propose a cascading deep generative adversarial network (GAN) in this paper, inspired by the workflow of urban experts. The first GAN is to build urban functional zones based on human instructions and surrounding contexts. The second GAN will produce the land-use configuration by considering the built urban functional zones. Finally, we conducted extensive experiments and case studies to validate the effectiveness and superiority of our work.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

Notes

  1. https://en.wikipedia.org/wiki/Kullback-Leibler_divergence

  2. https://en.wikipedia.org/wiki/Jensen-Shannon_divergence

  3. https://en.wikipedia.org/wiki/Hellinger_distance

  4. https://en.wikipedia.org/wiki/Cosine_similarity

References

  1. Litman T (2015) Analysis of public policies that unintentionally encourage and subsidize urban sprawl

  2. Public: Sidewalks. [EB/OL] (2022) https://www.sidewalklabs.com/toronto

  3. Public: Baidu AI City. [EB/OL] (2017) https://www.globenewswire.com/news-release/2017/12/20/1267217/0/en/Baidu-and-Xiongan-New-Area-Sign-Strategic-Agreement-to-Develop-Smart-City.html

  4. Cugurullo F (2020) Urban artificial intelligence: from automation to autonomy in the smart city. Front Sustain Cities 2:38

    Article  Google Scholar 

  5. Shen J, Liu C, Ren Y, Zheng H (2020) Machine learning assisted urban filling

  6. Ye X, Du J, Ye Y (2021) Masterplangan: facilitating the smart rendering of urban master plans via generative adversarial networks. Environ Plan B: Urban Anal City Sci, 23998083211023516

  7. Wang D, Fu Y, Wang P, Huang B, Lu C-T (2020) Reimagining city configuration: automated urban planning via adversarial learning. In: Proceedings of the 28th international conference on advances in geographic information systems, pp 497–506

  8. Dong H-W, Hsiao W-Y, Yang L-C, Yang Y-H (2018) Musegan: multi-track sequential generative adversarial networks for symbolic music generation and accompaniment. In: Thirty-second AAAI conference on artificial intelligence

  9. Wang D, Liu K, Johnson P, Sun L, Du B, Fu Y (2021) Deep human-guided conditional variational generative modeling for automated urban planning. In: 2021 IEEE international conference on data mining (ICDM), pp 679–688. https://doi.org/10.1109/ICDM51629.2021.00079

  10. Yuan NJ, Zheng Y, Xie X, Wang Y, Zheng K, Xiong H (2014) Discovering urban functional zones using latent activity trajectories. IEEE Trans Knowl Data Eng 27(3):712–725

    Article  Google Scholar 

  11. Kipf TN, Welling M (2016) Variational graph auto-encoders. arXiv preprint arXiv:1611.07308

  12. Ding Z, Xu Y, Xu W, Parmar G, Yang Y, Welling M, Tu Z (2020) Guided variational autoencoder for disentanglement learning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 7920–7929

  13. Zhang H, Xu T, Li H, Zhang S, Wang X, Huang X, Metaxas DN (2017) Stackgan: text to photo-realistic image synthesis with stacked generative adversarial networks. In: Proceedings of the IEEE international conference on computer vision, pp 5907–5915

  14. Larsen ABL, Sønderby SK, Larochelle H, Winther O (2016) Autoencoding beyond pixels using a learned similarity metric. In: ICML

  15. Yuan J, Zheng Y, Zhang C, Xie W, Xie X, Sun G, Huang Y (2010) T-drive: driving directions based on taxi trajectories. In: Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems, pp 99–108

  16. Mirza M, Osindero S (2014) Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784

  17. Sohn K, Lee H, Yan X (2015) Learning structured output representation using deep conditional generative models. Adv Neural Inf Process Syst 28:3483–3491

    Google Scholar 

  18. Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434

  19. Arjovsky M, Chintala S, Bottou L (2017) Wasserstein generative adversarial networks. In: Precup D, Teh YW (eds) Proceedings of the 34th international conference on machine learning. Proceedings of machine learning research, vol 70, pp 214–223

  20. Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville A (2017) Improved training of wasserstein gans. In: Proceedings of the 31st international conference on neural information processing systems, pp 5769–5779

  21. Meng T, Jing X, Yan Z, Pedrycz W (2020) A survey on machine learning for data fusion. Inf Fusion 57:115–129

    Article  Google Scholar 

  22. Wang D, Wang P, Liu K, Zhou Y, Hughes CE, Fu Y (2021) Reinforced imitative graph representation learning for mobile user profiling: an adversarial training perspective. Proc AAAI Conf Artif Intell 35:4410–4417

    Google Scholar 

  23. Liu K, Wang P, Zhang J, Fu Y, Das SK (2018) Modeling the interaction coupling of multi-view spatiotemporal contexts for destination prediction. In: Proceedings of the 2018 SIAM international conference on data mining, pp 171–179. SIAM

  24. Wang P, Liu K, Wang D, Fu Y (2021) Measuring urban vibrancy of residential communities using big crowdsourced geotagged data. Front Big Data 4

  25. Wang D, Wang P, Zhou J, Sun L, Du B, Fu Y (2020) Defending water treatment networks: Exploiting spatio-temporal effects for cyber attack detection. In: 2020 IEEE international conference on data mining (ICDM), pp 32–41. IEEE

  26. Ruthotto L, Haber E. An introduction to deep generative modeling. GAMM-Mitteilungen, 202100008

  27. Kobyzev I, Prince S, Brubaker M (2020) Normalizing flows: an introduction and review of current methods. IEEE Trans Pattern Anal Mach Intell

  28. Kingma DP, Welling M (2019) An introduction to variational autoencoders. arXiv preprint arXiv:1906.02691

  29. Creswell A, White T, Dumoulin V, Arulkumaran K, Sengupta B, Bharath AA (2018) Generative adversarial networks: an overview. IEEE Signal Process Mag 35(1):53–65

    Article  Google Scholar 

  30. Kang S, Cho K (2018) Conditional molecular design with deep generative models. J Chem Inf Model 59(1):43–52

    Article  Google Scholar 

  31. Chenthamarakshan V, Das P, Padhi I, Strobelt H, Lim KW, Hoover B, Hoffman SC, Mojsilovic A (2020) Target-specific and selective drug design for covid-19 using deep generative models

  32. Oliveira V, Pinho P (2010) Evaluation in urban planning: advances and prospects. J Plan Lit 24(4):343–361

    Article  Google Scholar 

  33. Naess P (2001) Urban planning and sustainable development. Eur Plan Stud 9(4):503–524

    Article  Google Scholar 

  34. Ratcliffe J, Stubbs M (2021) Urban planning and real estate development. Urban Planning and Real Estate Development

  35. Fistola R, Papa R (2016) Smart energy in the smart city. Urban planning for a sustainable future

  36. Nguyen NH (2020) Simulating the generative process of urban form: an application using opensim. J Plan Educ Res 40(4):393–404

    Article  Google Scholar 

  37. Wang D, Fu Y, Liu K, Chen F, Wang P, Lu C-T (2021) Automated urban planning for reimagining city configuration via adversarial learning: quantification, generation, and evaluation. ACM Transactions on Spatial Systems and Algorithms

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Central Florida, 4328 Scorpius Street, Orlando, FL, 32816-2362, USA

    Dongjie Wang & Yanjie Fu

  2. Department of Computer Science, Portland State University, 1825 SW Broadway, Portland, OR, 97201, USA

    Kunpeng Liu

  3. Department of Statistics, Southwestern University of Finance and Economics, Chengdu, 611130, Sichuan, China

    Yanyong Huang

  4. Department of Computer Science, Beihang University, Beijing, 100191, China

    Leilei Sun & Bowen Du

Authors
  1. Dongjie Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Kunpeng Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Yanyong Huang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Leilei Sun
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Bowen Du
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Yanjie Fu
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Yanjie Fu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, D., Liu, K., Huang, Y. et al. Automated urban planning aware spatial hierarchies and human instructions. Knowl Inf Syst 65, 1337–1364 (2023). https://doi.org/10.1007/s10115-022-01801-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-022-01801-6

Keywords