View in EDS

Application of deep learning algorithms in the design of urban subway public art space.

Saved in:
Bibliographic Details
Title: Application of deep learning algorithms in the design of urban subway public art space.
Authors: Wang, Qian1
Source: International Journal of Environment & Sustainable Development. 2026, Vol. 25 Issue 5, p44-72. 29p.
Subject Terms: Deep learning, Subways, Spatial arrangement, Multi-objective optimization, Public art spaces, Attention
Abstract: This paper aims to address the problem of insufficient integration of user visual attention behaviour modelling and spatial practicality in the design of subway public art spaces. This paper first constructs a cultural semantic labelling system and spatial attribute structure for subway stations based on deep neural networks. Second, it achieves multimodal deep alignment between semantic content and visual images through a contrastive language-image pre-training (CLIP) model. Then, it designs a multi-objective optimisation generation framework. Finally, it introduces a spatial structure adaptive analysis mechanism to achieve deep integration of generated content with the real subway environment. Experimental results show that each cultural category achieved high average scores of 0.9025 and 0.88 in terms of semantic consistency and accuracy of cultural background expression, respectively, indicating that the method performs well in terms of visual guidance effect and actual spatial foothold. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Environment & Sustainable Development is the property of Inderscience Enterprises Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: GreenFILE
Description
Abstract:This paper aims to address the problem of insufficient integration of user visual attention behaviour modelling and spatial practicality in the design of subway public art spaces. This paper first constructs a cultural semantic labelling system and spatial attribute structure for subway stations based on deep neural networks. Second, it achieves multimodal deep alignment between semantic content and visual images through a contrastive language-image pre-training (CLIP) model. Then, it designs a multi-objective optimisation generation framework. Finally, it introduces a spatial structure adaptive analysis mechanism to achieve deep integration of generated content with the real subway environment. Experimental results show that each cultural category achieved high average scores of 0.9025 and 0.88 in terms of semantic consistency and accuracy of cultural background expression, respectively, indicating that the method performs well in terms of visual guidance effect and actual spatial foothold. [ABSTRACT FROM AUTHOR]
ISSN:14746778
DOI:10.1504/IJESD.2026.151850