11 minute read
The lay of the land: A landscape perspective on artificial intelligence
AI-assisted rendering of landscape project. Nick Tyrer.
Denise Chevin sets out the basics of artificial intelligence, explores its application in landscape practice and highlights implications for the future of the profession.
From smart assistants like Amazon’s Alexa to spam filters on your email, to online chatbots and robotic vacuum cleaners, artificial intelligence (AI) has been incrementally impacting our everyday lives without many of us even registering it.
Over the past 18 months these advances have been moving at warp speed, with the development of so-called generative AI. New tools are fundamentally changing the way creative professionals work–landscape practitioners included.
Generative AI, in the form of ChatGPT, Microsoft Copilot and image creation models like Midjourney, DALL-E3 and Stable Diffusion, to name a few, has the potential to provide huge efficiencies in concept design and is taking the strain of more administrative activities like generating reports and writing submissions.
Coupled with other still-embryonic software, this offers huge potential in devising and planning landscape in consideration of biodiversity, climate and soil conditions. These tools can potentially help streamline workflows, reduce costs and ensure that the design is environmentally sound. The prospect is being met in the landscape community with an understandable mixture of trepidation about job security and excitement at the creative possibilities it offers and so at this stage its use comes with a health warning: AI in all its formats should be used as a starting-off point, not a panacea.
“We’re in the full-on experimental phase, where most people are playing with off-the-shelf products to see what’s possible,” says Phil Fernberg, Director of digital innovation at USbased landscape consultancy OJB and a member of the American Society of Landscape Architects, who holds a PhD in AI and landscape architecture. “But as far as full business transformation or workflow integration, that’s still in the beginning. People are still trying to figure out how to use it.”
So what is artificial intelligence?
AI is software that can analyse large amounts of data, recognise patterns and make predictions or decisions based on that data, continuously improving its performance over time. According to Fernberg, “AI is a whole umbrella of different ways of approaching automation and there are different ways of conceptualising and characterising it. One way is according to its abilities, of which there are three categories.”
These categories are, firstly, artificial ‘narrow intelligence’, which is the capability to perform a discrete task or a discrete set of tasks – like virtual assistants. Secondly, artificial ‘general intelligence’ is where it’s getting into the ability to perform generally on a wide range of tasks, just as humans would in novel environments and novel contexts and at the same rate as humans, like driving a car and this is still a work in progress. And then, thirdly, there’s artificial super intelligence, where it surpasses that of humans and is still in the realms of science fiction.
“When most people talk about AI these days they are talking about generative AI – so that’s programs like ChatGPT, DALL-E and Midjourney,” says Fernberg.
These types of programs are still classified as ‘narrow intelligence’ but operate at a game-changing level. They use deep learning from vast amounts of data taken from the internet (or internal documents) to analyse and understand text or images and then generate their own output based on prompts provided by the user.
When most people talk about AI these days they are talking about generative AI –so that’s programs like ChatGPT, DALL-E and Midjourney.
Alan King, head of global membership development at IMechE and founder of AIYourOrg. com, says, “If you’re asking AI to generate a picture of a cat, it’s looking at millions of pictures of cats and then asking itself, ‘What does a cat look like?’ Then it creates an image that it thinks answers your question.”
When it comes to landscape images, generative AI programs can generate multiple design variations, exploring numerous possibilities based on parameters like space constraints, environmental conditions and aesthetics. For landscape designers, bespoke software is being developed in the form of plug-ins to existing design that can help create optimised outdoor environments, adjust landscape layouts quickly, or create planting plans and assess irrigation needs.
‘Democratising’ landscape design
Such is the speed of development that it’s off-the-shelf general program that have been shaking up the design sphere because they are so easy to use.
Nick Tyrer is a practice leader in computational design and research at BDP Pattern, the sports and entertainment division of BDP. An architect by training, he has been using generative image tools like Midjourney to help create concepts for competition submissions where requirements are visual rather than necessarily requiring full landscape considerations.
“The AI image-generating tools are very good from a designer’s perspective, whether you’re an architect or a landscape designer. You can open the program and just start describing what you want and it will generate an image, based on your prompts. It might not be what you want, but it might be close and you can just keep honing it with more prompts. Alternatively, you can use it just for inspiration.”
Tyrer finds that current constraints are to do with biases in training data and potential reinforcement of existing stereotypes. For architects, that amounts, for example, to a prevalence of American-style architecture in AI-generated images of houses. He warns that, used naively, the tools can lead to a false sense of creativity and the designer simply presenting random, unoriginal content.
Tamae Isomura, senior landscape architect at Ground Control, echoes many of these sentiments from her experience of experimenting with AI tools. She observes how generative AI is a potential threat to the work of landscape designers, but is also providing benefits.
Ground Control is a design and build contractor which works entirely digitally, using traditional computer aided design (CAD), Geographical Information Systems (GIS) and 3D modelling. Plants are included in BIM models, specifying the plant species and Autodesk’s Revit is also used for planting plans.
Isomura says that they are using image-based generative AI mainly for concept development, brainstorming with the software to generate ideas, as well as improving design statements and identifying areas for improvement based on previous projects. The company is also testing Microsoft Copilot Pro, which can be customised by scanning and searching internal documents saved on the company server. In practice, Isomura says you might prompt the tool to write up the design statement from the rendered plan or drawings, which would produce a basic structure of the concepts and description. You might then ask how the design might be improved and the AI would search previous projects from the internal files saved on the server and suggest various ideas.
“It is useful as a time-saving tool. It’s not perfect but the pace of improvement at the moment is dramatic so we can see the potential,” she says.
Isomura also sees some potential downsides – and is concerned that AI could tempt some practices to replace junior roles in design, particularly in the early stages of projects, such as pre-planning concepts where the landscape professional’s input is often visual.
She also observes that Ground Control often finds that they will have to redesign schemes at the construction stage if the visual concepts are generated by others using AI without any landscape qualifications. “It might be that because of the retaining wall, the planting doesn’t work in a given area, because of the soil conditions or the micro-climate.”
Isomura adds that it is possible to use AI for planting while also taking into consideration the site conditions and site layout scenarios, but it needs to be validated by experts.
“For me, the important thing is that landscape practices do not replace the junior landscape architects with AI, because we need to develop the future generations to have human intelligence. We need to give people the opportunity to learn.”
Designers across the board flag up the issue of copyright as another major concern. Fernberg says the unattributed use of imagery has become like the “wild west”. He and others point to general confusion, user terms evolving on the hoof and the need for clear legal frameworks to be developed.
As well as the ethics of intellectual property and of creativity and creative agency in the design process, another worry for some is the environmental implications. “The data centres powering generative AI models are power hungry and therefore carbon intensive,” says Fernberg.
For me, the important thing is that landscape practices do not replace the junior landscape architects with AI, because we need to develop the future generations to have human intelligence. We need to give people the opportunity to learn.
What of the future?
There is an industry consensus that AI can be a useful assistant. But with all parts of the design process being pervaded in some way by AI, there is also a strong general view that it will only be a matter of time before these systems are joined up and the whole design process becomes more automated, with designers acting in the role of ‘’synthesiser”, as Fernberg describes it.
“You won’t see it for a while, but what you will see is that some of those individual elements of the design process will be made far more efficient and completely transformed. And they’ll link in with systems that were already around even before generative AI – like parametric design and greater machine learning models for spatial data – to transform the entire design process.”
Professor Andy Hudson Smith, from the Centre of Advanced Spatial Analysis at UCL, paints a similar picture. One of the projects he is involved with is converting Met Office data into written text and using AI to generate landscape images, to understand what AI can create from data feeds and whether it can generate realistic representations.
“The technology is advancing monthly. If you’re a landscape designer, you might cut and paste your client’s brief into an AI program and see what it comes up with. And it will probably come up with something which is wrong. But roll forward two or three years and it’ll probably come up with the plans, the drawings, look at the long-term flooding and storm water management, drought-resistant planning, carbon footprint and write your client report at the same time.”
“What is also attractive about generative AI is you can write software yourself. I’m an urban planner with a geography-based background, yet I can write software using ChatGPT and ask it to build the things that I want to do. So suddenly you become the expert in almost all things, because you can build the software.”
Alan King is also convinced of the huge impact AI will inevitably have.
“As we go forward, these models are going to become supercharged,” he says. “Most AI systems require a lot of human interaction and input and prompting to get to the kind of output you might desire. But over the next five to ten years, the capability is going to get better exponentially, with AI programs solving tasks together and taking humans out of a number of steps in the loop.”
“So you might produce a photograph of the land that needs to be developed, ask it to develop five possible designs and it will go away and do that. It will cost it, tell you where you can get all the materials you need and will pull the whole project together and give you the finished plan. And then it will be a case of choosing which one you’re going to work with.”
It seems that the industry is set to change significantly in the years ahead. But while much of what has historically been the job of a landscape designer may become automated, currently the expertise, skill and intelligence of a human would still be essential for choosing which of those five possible designs would be best. As long as that is the case, it will remain essential that landscape professionals are trained, educated and engaged in the process, so that the outcomes of projects are landscape-led. As Dr Fernberg says, it’s about being the synthesiser.
Denise Chevin MBE is a freelance writer and editor specialising in the built environment and is a former editor of Building Magazine.
