Posts Tagged ‘machine learning’


Advancements in Artificial Intelligence creativity should make us rethink the future of landscape architecture practice.



If you were to thumb through old issues of Science magazine, once you hit 1967 you would come across an obscure article coauthored by Allen Newell, an esteemed pioneer of artificial intelligence research, arguing for the validity of a new discipline called computer science. In the article, Newell and his colleagues Alan J. Perlis and Herbert A. Simon address some fundamental objections within academia to the idea that the study of computers was, in fact, a science or even a worthwhile pursuit. The questions are simple but fundamental: Is there such a thing as computer science? If so, what is it?

As you read the objections and their respective responses, you might begin to think as we did about the similar line of questioning that has been employed in landscape architecture. Substitute the computer speak with our own professional jargon and you have near carbon copies of themes from licensure advocacy meetings, ASLA conferences, or academic treatises on the state of the discipline. Computer science and landscape architecture have a surprising amount in common. They are both relatively new (at least in the official sense), they have both evolved in significant ways over the past century, and they both have been in an ongoing existential discussion about their position amid peer disciplines. This is nice to know but not revelatory.

Yet the intersection gets more interesting. One of the objections in the article states: “The term ‘computer’ is not well defined, and its meaning will change with new developments, hence computer science does not have a well-defined subject matter.” The authors’ reply is astute and resonant: “The phenomena of all sciences change over time; the process of understanding assures that this will be the case. Astronomy did not originally include the study of interstellar gases; physics did not include radioactivity; psychology did not include the study of animal behavior. Mathematics was once defined as the ‘science of quantity.’” So too is the phenomenon of landscape architecture; it just happens to work on an accelerated timeline. The field is ever shifting, retooling, and reassessing our place as our understanding of our medium and our instruments evolves. Before Olmsted, landscapes were gardens rather than systems; before Ian McHarg’s Design with Nature, those systems were not intertwined with ecology; before CAD, GIS, or Adobe, our only tools were pen and paper. (more…)

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Algorithms are bringing new kinds of evidence and predictive powers to the shaping of landscapes.



Tree. Person. Bike. Person. Person. Tree. Anya Domlesky, ASLA, an associate at SWA in Sausalito, California, rattles off how she and the firm’s innovation lab team train a computer to recognize the flora and fauna in an urban plaza.

The effort is part of the firm’s mission to apply emergent technologies to landscape architecture. In pursuing the applied use of artificial intelligence (AI) and machine learning, the research and innovation lab XL: Experiments in Landscape and Urbanism follows a small but growing number of researchers and practitioners interested in the ways the enigmatic yet ubiquitous culture of algorithms might be deployed in the field.

Examples of AI and machine learning are all around us, from the voice recognition software in your iPhone to the predictive software that drives recommendations for Netflix binges. While the financial and health care industries have quickly adopted AI, and use in construction and agriculture is steadily growing, conversations within landscape architecture as to how such tools translate to the design, management, and conservation of landscapes are still on the periphery for the field. This marginality may be because despite their everyday use, mainstream understandings of AI are clouded by clichés—think self-actualized computers or anthropomorphic robots. In a recent essay on Medium, Molly Wright Steenson, the author of Architectural Intelligence: How Designers and Architects Created the Digital Landscape (The MIT Press, 2017), argued that we need new clichés. “Our pop culture visions of AI are not helping us. In fact, they’re hurting us. They’re decades out of date,” she writes. “[W]e keep using the old clichés in order to talk about emerging technologies today. They make it harder for us to understand AI—what it is, what it isn’t, and what impact it will have on our lives.”

So then, what is a new vision—a vision of AI for landscape? (more…)

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A custom geodesign process aims to help prototype solutions for the health of a rural watershed.


Sam Ziegler, a corn-and-soybean farmer in southern Minnesota, had a chance to try out a new geodesign tool that could change the way he plants his crops. “It’s always on your mind what you can do better, but it’s hard to physically take an acre or a hundred out of production just to try something,” Ziegler says. “You can’t afford it. But this computer model allows you to play with things and get an understanding of what potentially would be the ramifications and benefits of switching things around.” The tool, operated with a touchscreen, was developed by a team of University of Minnesota (UM) researchers from the fields of landscape architecture, urban planning, economics, and soil and water science.

In the fall of 2013, the research team brought together about 40 community members, including farmers and environmental advocates, who were interested in improving the health of the Seven Mile Creek Watershed near Mankato, Minnesota. The group participated in a series of workshops that culminated with their generating alternative scenarios using an interactive computer model of the watershed. This investigation was supported by background layers such as aerial photographs, parcel lines, and topographic data that would feel familiar to regular users of geographic information systems. Using a 55-inch touchscreen, participants could assign various agricultural land uses to the landscape, including conventional corn and soybeans and perennial prairie grasses. “Basically, it was like painting a map, with some boundaries,” Ziegler says.

Once participants settled on a design, the geodesign program would analyze its environmental performance around various factors such as (more…)

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