AI-Generated Content in Landscape Architecture: A Survey
Yue Xing, Wensheng Gan, Qidi Chen, Philip S. Yu
TL;DR
This survey addresses the integration of AI-generated content (AIGC) into landscape architecture (LA) by outlining current LA challenges, potential AIGC-driven workflows, and the key technologies enabling AI-assisted design. It highlights applications across site research, concept generation, parametric optimization, plant configuration, and construction management, emphasizing data-driven decision support, visualization, and sustainability. The authors discuss critical challenges—data quality, design expertise, algorithm biases, user engagement, and ethics—and forecast trends toward interdisciplinary collaboration, broader tool ecosystems, and regulatory frameworks. The work provides a framework for practitioners to leverage AIGC for efficient, innovative, and sustainable LA design while acknowledging the need for governance and human-centered design judgment.
Abstract
Landscape design is a complex process that requires designers to engage in intricate planning, analysis, and decision-making. This process involves the integration and reconstruction of science, art, and technology. Traditional landscape design methods often rely on the designer's personal experience and subjective aesthetics, with design standards rooted in subjective perception. As a result, they lack scientific and objective evaluation criteria and systematic design processes. Data-driven artificial intelligence (AI) technology provides an objective and rational design process. With the rapid development of different AI technologies, AI-generated content (AIGC) has permeated various aspects of landscape design at an unprecedented speed, serving as an innovative design tool. This article aims to explore the applications and opportunities of AIGC in landscape design. AIGC can support landscape design in areas such as site research and analysis, design concepts and scheme generation, parametric design optimization, plant selection and visual simulation, construction management, and process optimization. However, AIGC also faces challenges in landscape design, including data quality and reliability, design expertise and judgment, technical challenges and limitations, site characteristics and sustainability, user needs and participation, the balance between technology and creativity, ethics, and social impact. Finally, this article provides a detailed outlook on the future development trends and prospects of AIGC in landscape design. Through in-depth research and exploration in this review, readers can gain a better understanding of the relevant applications, potential opportunities, and key challenges of AIGC in landscape design.