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Research ArticlePeer-Reviewed Articles

Artificial Intelligence in Landscape Architecture

A Literature Review

Phillip Fernberg and Brent Chamberlain
Landscape Journal, May 2023, 42 (1) 13-35; DOI: https://doi.org/10.3368/lj.42.1.13
Phillip Fernberg
Phillip Fernberg is a landscape designer, PhD candidate, and researcher in Utah State University’s Visualization, Instrumentation and Virtual Interaction Design (VIVID) Laboratory. He has earned an MLA from Louisiana State University and a BA in Latin American Studies from Brigham Young University. Fernberg’s current research focuses on spatial cognition in complex virtual environments and the implications of artificial intelligence for landscape architecture practice. He has published articles in several international journals and magazines and is a current recipient of the LAF Fellowship for Innovation and Leadership.
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Brent Chamberlain
Brent Chamberlain, PhD, is an associate professor of landscape architecture and environmental planning at Utah State University. His expertise as a computational environmental planner is built on three foci: 1) visualization and spatial data science, 2) applied computational approaches (including optimization and artificial intelligence), and 3) environmental perception and affect related to built and natural environments. His work has been published in several international journals, and his research has been funded by several national and state agencies, including the NSF, DoD, NIDILRR, UT DOT, and UT Public Lands. More can be found at:
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Abstract

The use of artificial intelligence (AI) is becoming increasingly common in landscape architecture. New methods and applications are proliferating yearly and are being touted as viable tools for research and practice. While researchers have conducted assessments of the state of AI-driven research and practice in allied disciplines, there is a knowledge gap for the same in landscape architecture. This literature review addresses this gap by searching and evaluating studies specifically focused on AI and disciplinary umbrella terms (landscape architecture, landscape planning, and landscape design). It includes searches of academic databases and industry publications that combine these umbrella terms with the main subfields of artificial intelligence as a discipline (machine learning, knowledge-based systems, computer vision, robotics, natural language processing, optimization). Initial searches returned over 600 articles, which were then filtered for relevance, resulting in about 100 articles that were reviewed in depth. The work highlights trends in dissemination, synthesizes emergent AI-Landscape (AI-LA) themes, and argues for unifying dissemination and compilation in research and practice so as not to lose relevant AI-LA knowledge and be caught off guard in the built environment profession’s next technological leap.

KEYWORDS
  • Landscape architecture
  • landscape design
  • landscape planning
  • machine learning
  • optimization
  • computational design
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Landscape Journal: 42 (1)
Landscape Journal
Vol. 42, Issue 1
1 May 2023
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Artificial Intelligence in Landscape Architecture
Phillip Fernberg, Brent Chamberlain
Landscape Journal May 2023, 42 (1) 13-35; DOI: 10.3368/lj.42.1.13

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Artificial Intelligence in Landscape Architecture
Phillip Fernberg, Brent Chamberlain
Landscape Journal May 2023, 42 (1) 13-35; DOI: 10.3368/lj.42.1.13
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  • Article
    • Abstract
    • INTRODUCTION
    • DEFINING REVIEW PARAMETERS
    • METHODOLOGY
    • REVIEW RESULTS AND TRENDS FOR AI-LA APPLICATIONS
    • DISCUSSION
    • CONCLUSION
    • Footnotes
    • REFERENCES
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  • Toward Increasing Faculty Licensure in Landscape Architecture Education
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Keywords

  • Landscape architecture
  • landscape design
  • landscape planning
  • machine learning
  • optimization
  • computational design
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