Year: 2025
Type: article
Abstract: In the digital age, AI technology has become a focal point in the field of land-scape architecture, with research primarily focusing on machine learning and neural networks. This study, grounded in the development of generative models and digital landscape technology, employs design thinking to explore the image generation pathways of generative AI models in landscape architecture. The findings indicate that the workflow proposed by general-purpose AI algo-rithms has certain limitations. By leveraging landscape architecture expertise for data augmentation and model fine-tuning, the speed and efficiency of AI-assisted landscape architecture image generation systems can be significant-ly enhanced.
DOI: 10.63313/aerpc.9026
Authors: Fengyu Xing, Lili Wu
Institution: Yangtze University
Language: English
Subfield: Architecture
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4411430156