Enhancing Urban Landscape Design: A GAN-Based Approach for Rapid Color Rendering of Park Sketches

Ran Chen, Jing Zhao (), Xueqi Yao, Yueheng He, Yuting Li, Zeke Lian, Zhengqi Han, Xingjian Yi and Haoran Li
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Ran Chen: School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China
Jing Zhao: School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China
Xueqi Yao: School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China
Yueheng He: School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China
Yuting Li: School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China
Zeke Lian: Ningbo City College of Vocational Technology, Ningbo 315100, China
Zhengqi Han: School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China
Xingjian Yi: School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China
Haoran Li: China United Network Communications Group Co., Beijing 100031, China

Land, 2024, vol. 13, issue 2, 1-16

Abstract: In urban ecological development, the effective planning and design of living spaces are crucial. Traditional color plan rendering methods, mainly using generative adversarial networks (GANs), rely heavily on edge extraction. This often leads to the loss of important details from hand-drawn drafts, significantly affecting the portrayal of the designer’s key concepts. This issue is especially critical in complex park planning. To address this, our study introduces a system based on conditional GANs. This system rapidly converts black-and-white park sketches into comprehensive color designs. We also employ a data augmentation strategy to enhance the quality of the output. The research reveals: (1) Our model efficiently produces designs suitable for industrial applications. (2) The GAN-based data augmentation improves the data volume, leading to enhanced rendering effects. (3) Our unique approach of direct rendering from sketches offers a novel method in urban planning and design. This study aims to enhance the rendering aspect of an intelligent workflow for landscape design. More efficient rendering techniques will reduce the iteration time of early design solutions and promote the iterative speed of designers’ thinking, thus improving the speed and efficiency of the whole design process.

Keywords: hand-drawn sketch; image color rendering; generative adversarial networks; data augmentation; landscape design (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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