Artificial Intelligence in Generating 3D Visualisations of Urban Precincts
Engineering, IT, Mathematics and Statistics
- This internship is able to cover project costs for domestic students only.
- The Industry Partner has implemented appropriate preparations to comply with Federal and State Government requirements regarding COVID-19 safety.
- If your skillset is aligned with this internship and you are located remotely, please enquire with the Internship Contact to discuss possible arrangements.
- This internship is exclusive to PhD students in Victoria or Queensland. Any applicants not meeting this requirement will automatically be deemed ineligible for this project.
ABOUT THE INDUSTRY PARTNER
Veris is the largest provider of surveying, planning and spatial services in Australia. Veris has 20 offices distributed across Australia, and a team of almost 500 professionals. Built through the acquisition and integration of some of the largest and most innovative businesses in the industry, Veris has a unique capability to service large multi-faceted projects in the infrastructure, resource and property sectors. To continue as a market leader Veris is extending its technology capabilities to take advantage of its vast quantity of data in order to provide advanced insights, knowledge and visualisations to its clients.
WHAT’S IN IT FOR YOU?
- Work with an award-winning urban planning and design team
- Develop an understanding of the property development process
- Work with leading spatial datasets in creating 3D visualisations
- Connect with industry leaders across survey, spatial and urban planning
RESEARCH TO BE CONDUCTED
Visualisations of new precincts in the planning phase are important for community engagement, planning approvals, marketing and optimising design. However, 3D visualisations are often costly and can be time-intensive to produce. Given the process is largely manual the ability for planners and developers to make changes to the design results in additional costs for 3D visualisation. Often what is needed is a low-cost basic 3D model to get a sense of volume, demonstrate views and a sense of space, assist with site constraint troubleshooting at early design phase and the development of innovative tailored solutions. Having a low-cost visualisation where tweaks can be made and quickly visualised will also help optimise precinct design.
This research aims to use artificial intelligence to build low-cost 3D visualisations of urban planning precincts. The concept will involve generating the visualisation using:
- The housing code converted to programmable rules as code or enabled as user defined parameters.
- A 2D CAD design.
- A digital elevation model.
- User defined parameters such as materials, curb style, vegetation types and aesthetics preferences.
- A library of standard 3D objects and reference material.
The research should produce an algorithm which can quickly generate the 3D visualisation from these range of data sources.
SKILLS WISH LIST
If you’re a PhD student and meet some or all the below we want to hear from you. We strongly encourage women, indigenous and disadvantaged candidates to apply:
- The ability to work effectively as part of a multi-disciplinary, potentially regionally dispersed team, plus the motivation and discipline to carry out independent research.
- Solid knowledge of computer vision and machine learning, and the ability to understand and develop mathematically founded algorithms and their development in toolkits such as TensorFlow or PyTorch.
- High level computational and programming skills (in Python or C++) to build computer vision/machine learning models and conduct analyses.
- A record of science innovation and creativity, including the ability & willingness to incorporate novel ideas and approaches into scientific investigations.
- A good understanding of 3D spaces and grasp of 3D visualisations.
- Urban design skills are desirable.
The research outcomes will be a brief report (3-5 pages) of current developments in using artificial intelligence to produce 3D visualisations. The main research output will be an algorithm that will produce a 3D visualisation based on input data sources. Accompanying the algorithms will be a short report on testing, deployment and the code repository.
The intern will receive $3,000 per month of the internship, usually in the form of stipend payments.
It is expected that the intern will primarily undertake this research project during regular business hours and maintain contact with their academic mentor throughout the internship either through face-to-face or phone meetings as appropriate.
The intern and their academic mentor will have the opportunity to negotiate the project’s scope, milestones and timeline during the project planning stage.
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