Mapping approach for emotional response to urban visual environments based on street view images and EEG signals
文献类型：期刊论文
| 作者 | Liu, Lin5; Gan, Xiwei5; Ren, Zhoupeng4; Hang, Jian3; Zhang, Xiaolin2; Ji, Yuchen1 |
| 刊名 | BUILDING SIMULATION
 |
| 出版日期 | 2025-09-22 |
| 卷号 | N/A |
| 关键词 | urban landscape emotional response deep learning street view image EEG |
| ISSN号 | 1996-3599 |
| DOI | 10.1007/s12273-025-1344-5 |
| 产权排序 | 2 |
| 文献子类 | Article ; Early Access |
| 英文摘要 | Urban visual environment plays a critical role in city planning and public health by influencing residents' emotional responses. However, existing studies rely on subjective assessments of small-scale areas, lacking objective, large-scale emotional maps. This study introduces a novel electroencephalogram (EEG)-based framework to quantify emotional responses to urban visual environments. First, urban visual indicators are extracted from 352,112 street view images (SVI) in Guangzhou and 680,280 in Shenzhen using a deep learning-based semantic segmentation. Then, EEG experiments are conducted with 24 participants exposed to four groups of SVI visual stimuli, employing machine learning models to quantify relationships between visual indicators and emotions. Finally, a series of models are integrated to generate city-wide visual emotional maps across four emotion dimensions and valence-arousal space. Results show that the models established between the visual environment and emotional responses display R-2 values in the range of 0.39 to 0.69, enabling visual emotional mapping. Correlation analysis further shows a higher green view index and color entropy significantly correlate with positive emotions (p < 0.01). Conversely, elevated building density and openness are linked to negative emotions (p < 0.01). In terms of HAPV (high-arousal, positive-valence) dimensions, Shenzhen had higher emotion scores than Guangzhou, with mean values of 0.237 and 0.226, respectively. These differences correspond to colorful urban center landscapes in Shenzhen and predominantly green landscapes in Guangzhou as a result of their different urban planning strategy. This study contributes to the optimization and transformation of urban landscapes for improved visual comfort. |
| URL标识 | 查看原文 |
| WOS关键词 | SELF-ASSESSMENT MANNEQUIN ; BRAIN ; SCENES |
| WOS研究方向 | Thermodynamics ; Construction & Building Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001577107600001 |
| 出版者 | TSINGHUA UNIV PRESS |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/216049]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Liu, Lin |
| 作者单位 | 1.Shenzhen Univ, Coll Mechatron & Control Engn, Shenzhen 518060, Peoples R China 2.Guangdong Univ Technol, Acad Art & Design, Dept Digital Media & Animat, 729 Dongfeng East Rd, Guangzhou 510090, Peoples R China; 3.Sun Yat Sen Univ Zhuhai, Sch Atmospher Sci, Zhuhai 519082, Peoples R China; 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; 5.Guangdong Univ Technol, Sch Civil & Transportat Engn, Guangzhou 510006, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Liu, Lin,Gan, Xiwei,Ren, Zhoupeng,et al. Mapping approach for emotional response to urban visual environments based on street view images and EEG signals[J]. BUILDING SIMULATION,2025,N/A. |
| APA | Liu, Lin,Gan, Xiwei,Ren, Zhoupeng,Hang, Jian,Zhang, Xiaolin,&Ji, Yuchen.(2025).Mapping approach for emotional response to urban visual environments based on street view images and EEG signals.BUILDING SIMULATION,N/A. |
| MLA | Liu, Lin,et al."Mapping approach for emotional response to urban visual environments based on street view images and EEG signals".BUILDING SIMULATION N/A(2025). |
入库方式： OAI收割
来源：地理科学与资源研究所
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