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Emotional-Health-Oriented Urban Design: A Novel Collaborative Deep Learning Framework for Real-Time Landscape Assessment by Integrating Facial Expression Recognition and Pixel-Level Semantic Segmentation

Emotional responses are significant for understanding public perceptions of urban green space (UGS) and can be used to inform proposals for optimal urban design strategies to enhance public emotional health in the times of COVID-19. However, most empirical studies fail to consider emotion-oriented l...

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Autores principales: Zhang, Xuan, Han, Haoying, Qiao, Lin, Zhuang, Jingwei, Ren, Ziming, Su, Yang, Xia, Yiping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9603572/
https://www.ncbi.nlm.nih.gov/pubmed/36293893
http://dx.doi.org/10.3390/ijerph192013308
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author Zhang, Xuan
Han, Haoying
Qiao, Lin
Zhuang, Jingwei
Ren, Ziming
Su, Yang
Xia, Yiping
author_facet Zhang, Xuan
Han, Haoying
Qiao, Lin
Zhuang, Jingwei
Ren, Ziming
Su, Yang
Xia, Yiping
author_sort Zhang, Xuan
collection PubMed
description Emotional responses are significant for understanding public perceptions of urban green space (UGS) and can be used to inform proposals for optimal urban design strategies to enhance public emotional health in the times of COVID-19. However, most empirical studies fail to consider emotion-oriented landscape assessments under dynamic perspectives despite the fact that individually observed sceneries alter with angle. To close this gap, a real-time sentimental-based landscape assessment framework is developed, integrating facial expression recognition with semantic segmentation of changing landscapes. Furthermore, a case study using panoramic videos converted from Google Street View images to simulate changing scenes was used to test the viability of this framework, resulting in five million big data points. The result of this study shows that through the collaboration of deep learning algorithms, finer visual variables were classified, subtle emotional responses were tracked, and better regression results for valence and arousal were obtained. Among all the predictors, the proportion of grass was the most significant predictor for emotional perception. The proposed framework is adaptable and human-centric, and it enables the instantaneous emotional perception of the built environment by the general public as a feedback survey tool to aid urban planners in creating UGS that promote emotional well-being.
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spelling pubmed-96035722022-10-27 Emotional-Health-Oriented Urban Design: A Novel Collaborative Deep Learning Framework for Real-Time Landscape Assessment by Integrating Facial Expression Recognition and Pixel-Level Semantic Segmentation Zhang, Xuan Han, Haoying Qiao, Lin Zhuang, Jingwei Ren, Ziming Su, Yang Xia, Yiping Int J Environ Res Public Health Article Emotional responses are significant for understanding public perceptions of urban green space (UGS) and can be used to inform proposals for optimal urban design strategies to enhance public emotional health in the times of COVID-19. However, most empirical studies fail to consider emotion-oriented landscape assessments under dynamic perspectives despite the fact that individually observed sceneries alter with angle. To close this gap, a real-time sentimental-based landscape assessment framework is developed, integrating facial expression recognition with semantic segmentation of changing landscapes. Furthermore, a case study using panoramic videos converted from Google Street View images to simulate changing scenes was used to test the viability of this framework, resulting in five million big data points. The result of this study shows that through the collaboration of deep learning algorithms, finer visual variables were classified, subtle emotional responses were tracked, and better regression results for valence and arousal were obtained. Among all the predictors, the proportion of grass was the most significant predictor for emotional perception. The proposed framework is adaptable and human-centric, and it enables the instantaneous emotional perception of the built environment by the general public as a feedback survey tool to aid urban planners in creating UGS that promote emotional well-being. MDPI 2022-10-15 /pmc/articles/PMC9603572/ /pubmed/36293893 http://dx.doi.org/10.3390/ijerph192013308 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhang, Xuan
Han, Haoying
Qiao, Lin
Zhuang, Jingwei
Ren, Ziming
Su, Yang
Xia, Yiping
Emotional-Health-Oriented Urban Design: A Novel Collaborative Deep Learning Framework for Real-Time Landscape Assessment by Integrating Facial Expression Recognition and Pixel-Level Semantic Segmentation
title Emotional-Health-Oriented Urban Design: A Novel Collaborative Deep Learning Framework for Real-Time Landscape Assessment by Integrating Facial Expression Recognition and Pixel-Level Semantic Segmentation
title_full Emotional-Health-Oriented Urban Design: A Novel Collaborative Deep Learning Framework for Real-Time Landscape Assessment by Integrating Facial Expression Recognition and Pixel-Level Semantic Segmentation
title_fullStr Emotional-Health-Oriented Urban Design: A Novel Collaborative Deep Learning Framework for Real-Time Landscape Assessment by Integrating Facial Expression Recognition and Pixel-Level Semantic Segmentation
title_full_unstemmed Emotional-Health-Oriented Urban Design: A Novel Collaborative Deep Learning Framework for Real-Time Landscape Assessment by Integrating Facial Expression Recognition and Pixel-Level Semantic Segmentation
title_short Emotional-Health-Oriented Urban Design: A Novel Collaborative Deep Learning Framework for Real-Time Landscape Assessment by Integrating Facial Expression Recognition and Pixel-Level Semantic Segmentation
title_sort emotional-health-oriented urban design: a novel collaborative deep learning framework for real-time landscape assessment by integrating facial expression recognition and pixel-level semantic segmentation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9603572/
https://www.ncbi.nlm.nih.gov/pubmed/36293893
http://dx.doi.org/10.3390/ijerph192013308
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