987
Views
2
CrossRef citations to date
0
Altmetric
Research Articles

A topography-aware approach to the automatic generation of urban road networks

, , , , &
Pages 2035-2059 | Received 11 Aug 2021, Accepted 28 Apr 2022, Published online: 01 Jun 2022

References

  • Aliaga, D.G., Vanegas, C.A., and Benes, B., 2008. Interactive example-based urban layout synthesis. ACM Transactions on Graphics, 27 (5), 1–10.
  • Beneš, J., Wilkie, A., and Křivánek, J., 2014. Procedural modelling of urban road networks. Computer Graphics Forum, 33 (6), 132–142.
  • Boeing, G., 2019. Urban spatial order: street network orientation, configuration, and entropy. Applied Network Science, 4 (1), 1–19.
  • Chen, G., et al., 2008. Interactive procedural street modeling. ACM SIGGRAPH 2008 papers. 1–10.
  • Coons, S.A., 1964. An outline of the requirements for a computer-aided design system. Simulation, 2 (2), R-2–304.
  • Fang, Z., et al., 2020a. "Reading" cities with computer vision: a new multi-spatial scale urban fabric dataset and a novel convolutional neural network solution for urban fabric classification tasks. In: Proceedings of the 28th International Conference on Advances in Geographic Information Systems (SIGSPATIAL '20), Seattle, WA, USA 3-6 November 2020, 507–517.
  • Fang, Z., Jin, Y., and Yang, T., 2021. Incorporating planning intelligence into deep learning: A planning support tool for street network design. Journal of Urban Technology, 1–16.doi:10.1080/10630732.2021.2001713.
  • Fang, Z., Yang, T., and Jin, Y., 2020b. DeepStreet: A deep learning powered urban street network generation module. arXiv preprint arXiv: 2010.04365. doi:10.48550/arXiv.2010.04365.
  • Galin, E., et al., 2010. Procedural generation of roads. Computer Graphics Forum, 29 (2), 429–438.
  • Hartmann, S., et al., 2017. Streetgan: Towards road network synthesis with generative adversarial networks. In: Proceedings of the 25th International Conference in Central Europe on Computer Graphics, Visualization and Computer Visionin, 133–142.
  • Iizuka, S., Simo-Serra, E., and Ishikawa, H., 2017. Globally and locally consistent image completion. ACM Transactions on Graphics, 36 (4), 1–14.
  • Jo, Y., and Park, J., 2019. Sc-fegan: Face editing generative adversarial network with user's sketch and color. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 1745–1753.
  • Kelvin, L.Z., and Anand, B., 2020. Procedural Generation of Roads with Conditional Generative Adversarial Networks. In: 2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM), 277–281.
  • Kempinska, K., and Murcio, R., 2019. Modelling urban networks using Variational Autoencoders. Applied Network Science, 4 (1), 1–11.
  • Law, S., et al., 2020. Street-Frontage-Net: urban image classification using deep convolutional neural networks. International Journal of Geographical Information Science, 34 (4), 681–707.
  • Law, S., and Neira, M., 2019. An unsupervised approach to geographical knowledge discovery using street level and street network images. In: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, GeoAI, 56–65.
  • Miyato, T., et al., 2018., Spectral normalization for generative adversarial networks. arXiv preprint arXiv:1802.05957.
  • Olaya, V., 2009. Basic land-surface parameters. Developments in Soil Science, 33, 141–169.
  • Parish, Y.I.H., and Müller, P., 2001. Procedural modeling of cities. In: Proceedings of the 28th annual conference on Computer graphics and interactive techniques. 301–308.
  • Shi, W., et al., 2021. RegNet: a neural network model for predicting regional desirability with VGI data. International Journal of Geographical Information Science, 35 (1), 175–192.
  • Yang, T., 2020. Understanding commuting patterns and changes: Counterfactual analysis in a planning support framework. Environment and Planning B: Urban Analytics and City Science, 47 (8), 1440–1455.
  • Yang, T., et al., 2019. Aspirations and realities of polycentric development: Insights from multi-source data into the emerging urban form of Shanghai. Environment and Planning B: Urban Analytics and City Science, 46 (7), 1264–1280.
  • Yu, J., et al., 2018., Generative image inpainting with contextual attention. arXiv preprint arXiv:1801.07892.
  • Yu, J., et al., 2019., Free-form image inpainting with gated convolution. arXiv preprint arXiv:1806.03589.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.