by Lauren Schmidt, PLA, ASLA
This post is a summary and recap of the presentation Putting AI to Work: Practical Applications of AI in Landscape Architecture that took place at the ASLA Conference on Landscape Architecture in Minneapolis this past October. This post was written with the assistance of ChatGPT. The full PDF of the presentation is also provided at the end of the article.
Presentation by:
- Benjamin George, ASLA, Utah State University
- Phil Fernberg, ASLA, OJB
- Qing Luo, ASLA, Oklahoma State University
- Lauren Schmidt, PLA, ASLA, Parallax Team
- Tony Kostreski, PLA, ASLA, Vectorworks
- Matt Perotto, ASLA, Hargreaves Jones
Artificial intelligence (AI) has become a transformative force in various industries, and landscape architecture is no exception. This presentation explores the practical applications of AI in landscape architecture, featuring insights from experts in the field.
Understanding Artificial Intelligence and Machine Learning
AI, a concept contemplated for centuries, is the development of computer systems that mimic human intelligence. AI can be categorized into Artificial Narrow Intelligence (ANI) and Artificial General Intelligence (AGI). Landscape architecture benefits from ANI, which includes machine learning, knowledge-based systems, natural language processing, optimization, robotics, and computer vision.
The presentation breaks down the core components of AI, emphasizing machine learning (ML) as a subset that enables computers to learn without explicit programming. Deep learning (DL), a subset of ML using artificial neural networks, allows processing more complex patterns. ML is further categorized into supervised, unsupervised, and reinforcement learning, each playing a unique role in landscape architecture applications.