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Data-driven Urban Residential Layout Modelling

  • Full or part time
  • Application Deadline
    Tuesday, April 16, 2019
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

With the development of computer-aided design (CAD) techniques, the practical urban layout modelling has benefitted from software systems such as Civil3D and SiteOps. Even so, the current design process still largely depends on the manual sketching of all streets by urban designers. This requires significant input and severely limits the exploration of design possibilities.

Advanced procedural modelling tools then facilitate the design process by procedurally generating streets and parcels using computer algorithms. However, they require specialised knowledge to carefully define shape grammars/templates and generation rules, which is time-consuming, and restricts the quality and diversity of the results.

With the recent advances in artificial intelligence and data science, this project aims at a data-driven approach for modelling new residential layouts based on existing real-world layouts, leading to automated, efficient, and flexible layout modelling tools. The properties (geometry, topology, etc.) of the layouts will be learned from existing examples, which will then guide the modelling of new layouts with pre-defined user constraints, facilitating the exploration of high-dimensional design space.

This project will benefit several engineering applications such as civil engineering and environment engineering. It also has prospective societal impacts with creating better residential environment for people by efficiently planning new residential areas and re-planning existing areas.

This project is associated with the UKRI CDT in Accountable, Responsible and Transparent AI (ART-AI), which is looking for its first cohort of at least 10 students to start in September 2019. Students will be fully funded for 4 years (stipend, UK/EU tuition fees and research support budget). Further details can be found here: http://www.bath.ac.uk/research-centres/ukri-centre-for-doctoral-training-in-accountable-responsible-and-transparent-ai/.

Desirable qualities in candidates include intellectual curiosity, a strong background in maths and programming experience.

Applicants should hold, or expect to receive, a First Class or good Upper Second Class Honours degree. A master’s level qualification would also be advantageous.

Informal enquiries about the project should be directed Dr Yongliang Yang on email address .

Enquiries about the application process should be sent to .

Formal applications should be made via the University of Bath’s online application form for a PhD in Computer Science: https://samis.bath.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RDUCM-FP01&code2=0013

Start date: 23 September 2019.

Funding Notes

ART-AI CDT studentships are available on a competition basis for UK and EU students for up to 4 years. Funding will cover UK/EU tuition fees as well as providing maintenance at the UKRI doctoral stipend rate (£15,009 per annum for 2019/20) and a training support fee of £1,000 per annum.

We also welcome all-year-round applications from self-funded candidates and candidates who can source their own funding.

References

C.-H. Peng, Y.-L. Yang, F. Bao, D. Fink, D.-M. Yan, P. Wonka, and N. J. Mitra. Computational network design from functional specifications. ACM Trans. Graph., 35(4):131:1–131:12, 2016.
C.-H. Peng, Y.-L. Yang, and P. Wonka. Computing layouts with deformable templates. ACM Trans. Graph., 33(4):99:1–99:11, 2014.
Y.-L. Yang, J. Wang, E. Vouga, and P. Wonka. Urban pattern: Layout design by hierarchical domain splitting. ACM Trans. Graph., 32(6):181:1–181:12, 2013.

How good is research at University of Bath in Computer Science and Informatics?

FTE Category A staff submitted: 24.00

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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