From Design to GeoAI: Building the Intelligent Practice

An interview with Dr. Hossein Rizeei

Interview by Roderic Maclauchland

Dr. Hossein Rizeei was recently interviewed about his position on geospatial AI in design practice by Roderic Maclauchland, an events strategist and content developer who specialises in connecting leaders and decision-makers across the built environment and energy sectors.

Read the article below:

“Dr. Hossein Rizeei is a Geospatial Scientist working at the intersection of GIS, GeoAI, digital twins, remote sensing, masterplanning, urban design and landscape architecture. As BioUrbanism Lead at McGregor Coxall, he leads the development of spatial analytics and digital technologies that help design and planning teams understand complex urban and environmental systems, evaluate design and development scenarios, and make evidence-based decisions on projects ranging from site scale landscape work through to precinct masterplans and city scale urban strategies. His work spans geospatial intelligence, environmental modelling, remote sensing, AI driven analysis and digital twin technologies, with a focus on turning complex spatial data into practical insight for urbanism, masterplanning, landscape architecture and environmental planning.

Hossein will join the Digital Built World Summit in Sydney on 2 and 3 March to discuss how AI and geospatial technologies are changing the way urban and landscape environments can be understood, designed and delivered. In advance, we asked him three questions.

1. Automation is becoming a much bigger conversation in design. How should design and planning practices think about automation and systems thinking as AI becomes more capable?

Hossein – I see automation less as a replacement for design thinking and more as a change to the systems that support it. Across landscape, urban design and masterplanning, a lot of time goes into collecting, cleaning, processing and translating spatial and environmental data before anyone can make an informed decision. That workload only grows as projects scale up. A precinct masterplan might draw on cadastre, topography, hydrology, transport networks, demographics, zoning, utilities and climate data all at once. AI and automation can link those stages together, so geospatial data is processed automatically, patterns are flagged, environmental and urban analyses run, and the results feed straight into design and decision support. Get that right and information moves from data to analysis to design without stalling, which means people spend less time on repetitive technical work and more on interpretation, creativity and strategy.

2. Beyond the hype around AI generated imagery, what practical applications do you see for AI within landscape architecture and urban design?

Hossein – The most valuable applications go well beyond rendering and visualisation, although image generation has earned its place. It sits in our daily workflows now and it has freed up real time for design and iteration. Ideas that used to take days to visualise can be tested in an afternoon, which changes how many options a team can realistically explore. What interests me is that the same compression is now becoming possible on the analytical side.

A through things such as Claude Code is at a stage it can already process large volumes of spatial and environmental data, pick up patterns, classify landscapes and urban typologies, analyse vegetation, land cover and built form, model environmental conditions and surface relationships that are hard to detect manually. Paired with our own subject matter expertise at the masterplanning scale, it supports rapid option testing across yield and density studies, walkability and accessibility, solar access, wind and urban heat performance, flood exposure and green infrastructure distribution. Structure plans and urban frameworks can then be interrogated quantitatively rather than assumed.

Our team is also using AI to build custom web application prototypes. These are lightweight, project specific tools that let a team or a client interrogate options live instead of waiting on a static report. Put that alongside the GeoAI integrations we are testing in the Lab at regional urban design scale, and you start to see a workflow where visualisation, analysis and options testing all move at the same speed. The real value sits in combining AI with the GIS, remote sensing and modelling workflows we already run. It sharpens professional judgement rather than replacing it, and it makes the analysis and decision making behind a project faster, better informed and easier to iterate.

3. What happens when GeoAI, GIS and mapping become integrated?

Hossein – This is where the biggest change is happening. GIS has always been good at telling us where things are and how spatial relationships work. GeoAI adds the ability to detect patterns, make predictions and draw insight out of increasingly complex datasets. Connect those capabilities to digital twins and design workflows and the map stops being a representation of the site. It becomes an analytical and decision-making environment. We can build spatial models that bring environmental, social, ecological and urban data together, test scenarios and get feedback on their likely impacts. For masterplanning and urban design this matters, because development scenarios can be measured against real performance criteria such as climate resilience, biodiversity, mobility, liveability and infrastructure capacity before a plan is locked in. Across landscape architecture, urban design and masterplanning it opens up a far more intelligent spatial workflow. Understand a place, model how it behaves, test interventions, then use that evidence to inform the design.

Dr. Hossein Rizeei will explore these questions and the intersection of AI, automation and geospatial intelligence at the Digital Built World Summit. Find out more about the event and program: https://digitalbuiltworldsummit.com/”