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Abstract

Existing methods for evaluating urban greenway restorative environments lack objectivity, efficiency, and theoretical integration. The purpose of this research is to develop a restorative environmental assessment framework for urban road binding using deep learning, street-view image data, and environmental psychology theory. It uses a semantic segmentation model called DeepLabV3+ to collect six visual environment features which are otherwise difficult to represent numerically. At the same time, it offers a methodological path for the interdisciplinary integration of artificial intelligence technology and environmental psychology theory. The calculation model of the comprehensive recovery index is constructed in four dimensions based on attention recovery theory. According to empirical analysis, this framework can successfully identify systematic differences in the restorative dimension of different types of binding paths. The presence of greenness can make a large positive contribution to the restorative effect, while building occlusions can have an inhibitory effect. The evaluation results are quite consistent with theoretical predictions and have good robustness in parameter Settings. The findings of the study offer a scientific evaluation tool for accurate diagnosis and optimization improvement of the urban road binding restorative environment. At the same time, it offers a methodological path for the interdisciplinary integration of artificial intelligence technology and environmental psychology theory.

Keywords

Urban greenway Restorative environment Deep learning Street view image Attention recovery theory

Article Details

Author Biographies

Xueyan Jing, Faculty of Design and Architecture, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia

XUE-YAN JING is currently pursuing her PhD with the Faculty of Design and Architecture, Universiti Putra Malaysia, Serdang Selangor, Malaysia. Her research interests include Urban Planning and Design, Restorative Environment, Urban Public Space.

Mohd Fabian Hasna, Faculty of Design and Architecture, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia

Dr. Mohd Fabian Hasna received his PhD from Monash University, Australia. He is currently a PhD supervisor in the Faculty of Design and Architecture at Universiti Putra Malaysia.
Fields of Expertise: Restorative Environment, Public Art, Urban Public Space.

Aini Jasmin Ghazalli, Faculty of Design and Architecture, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia

Dr. Aini Jasmin Ghazalli received her PhD in Environment from the Australian National University. She is currently a PhD supervisor in the Faculty of Design and Architecture at Universiti Putra Malaysia. Field of Expertise: Landscape and well-being.

How to Cite
Jing, X., Fabian Hasna, M., & Jasmin Ghazalli, A. (2026). AI-driven assessment of urban greenway restorative environments: integrating deep learning, street view imagery, and environmental psychology . Future Technology, 5(2), 228–240. Retrieved from https://fupubco.com/futech/article/view/758
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