Impact of Streetscape Built Environment Characteristics on Human Perceptions Using Street View Imagery and Deep Learning: A Case Study of Changbai Island, Shenyang
Abstract
1. Introduction
- (1)
- Delineate the study area and determine the sample points in ArcGIS.
- (2)
- Quantify physical street characteristics via semantic segmentation technology and extract key morphological indices, such as green visibility rate and interface continuity.
- (3)
- Obtain residents’ street perception data using the Place Pulse 2.0 dataset.
- (4)
- Explore the relationship between physical street attributes and residents’ emotional perceptions.
2. Literature Review
2.1. Theoretical Studies Related to Human Perception
2.2. Human Perception Assessment of Urban Streets
2.3. Assessment of the Physical Characteristics of Urban Streets
2.4. Methods for Acquiring Human Perceptual Features
2.5. Methods for Obtaining the Physical Spatial Characteristics of Streets
3. Materials and Methods
3.1. Study Site
3.2. Baidu Street View Image Collection
3.3. Street Space Environment Indicator Extraction
3.4. Calculating Public Visual Perception Data
3.5. Statistical Analysis
4. Results
4.1. Physical Characteristics of the Study Area
4.2. Perceptual Characterization Data Results
4.3. The Effect of the Physical Characteristics of the Built Environment of Streets on Perceived Features
4.3.1. Correlation Analysis
4.3.2. Regression Analysis
- (1)
- Multiple linear regression analysis of beauty perception
- (2)
- Multiple linear regression analysis of safety perception
- (3)
- Multiple linear regression analysis of liveliness perception
- (4)
- Multiple linear regression analysis of boredom perception
5. Discussion
5.1. Perceptual and Physical Characteristics of Urban Streets
5.2. Relationship Between Physical and Perceptual Characteristics
5.3. Optimization Strategies Proposed by the Study
5.4. Limitations of This Study
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Appendix B
References
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Street Functional Type | Description | Typical Road Section |
---|---|---|
Transportation street | Transportation streets are characterized by continuous closed interfaces. These streets prioritize maximizing motor vehicle movement efficiency, focusing on facilities such as bus lanes. | Example: 2, 7, 22, 38, 70 |
Residential street | Residential streets serve as composite functional spaces dominated by residential use, where citizens’ daily life and activities are concentrated. These streets constitute linear public space systems anchored by small- and medium-sized commercial services, cultural stations, and public facilities. | Example: 1, 3, 4, 5, 76, 78, 8, 9 |
Commercial street | Commercial streets are linear public spaces where commercial activities—such as retail, dining, and office uses—predominate, characterized by distinct service capacities or industrial clusters. | Example: 99, 94, 45, 48 |
Comprehensive street | Comprehensive streets exhibit diverse functions and activities along the roadway, integrating the characteristics of two or more street types (e.g., commercial, lifestyle services, transportation, and landscape–recreation). | Example: 52, 62, 64, 67, 69, 68, 31 |
Physical Indicators | Formula | Interpretation | Definition |
---|---|---|---|
Green visual index | V1i and T1i denote the proportions of vegetation and terrain pixels, respectively. n is the number of BSV images of a sample point. | It refers to the ratio of tree and grass pixels to the overall pixels. | |
Openness | S1i denotes the proportion of sky pixels; the sum indicates the total number of sky pixels in each image. | It refers to the open degree in the coastal interface. | |
Walkable streets | P1i denotes the proportion of pavement pixels; F1i denotes the proportion of fence pixels; R1i denotes the proportion of road pixels. | It refers to the ratio of walkable street pixels to the overall pixels. | |
Vehicle occurrence rate | C1i denotes the proportion of car pixels; T2i denotes the proportion of truck pixels; B1i denotes the pro- portion of bus pixels; T3i denotes the proportion of train pixels; R1i denotes the proportion of road pixels. | It refers to the proportion of vehicle attendance in the road space. | |
Natural to artificial ratio of the vertical interface | T1i denotes the proportion of tree pixels; B2i denotes the proportion of building pixels. | It refers to the ratio of natural pixels to artificial pixels in the vertical interface. | |
Pedestrians | P3i denotes the proportion of pedestrian pixels; the sum indicates the total number of pedestrian pixelsin each image. | It refers to the ratio of pedestrian pixels to the overall street space pixels, including riders and standing or sitting pedestrians. | |
Building enclosure | B2i denotes the proportion of building pixels. | It refers to the ratio of building pixels to the overall pixels. |
Human Perception | Minimum | Minimum | Mean | Standard Deviation |
---|---|---|---|---|
Beauty | 0.181 | 0.873 | 0.411 | 0.201 |
Boredom | 0.314 | 0.626 | 0.414 | 0.073 |
Safety | 0.240 | 0.824 | 0.454 | 0.172 |
Liveliness | 0.253 | 0.846 | 0.661 | 0.140 |
Human Perception | Residential Streets | Transportation Streets | Commercial Streets | Comprehensive Streets | ||||
---|---|---|---|---|---|---|---|---|
Mean | Standard Deviation | Mean | Standard Deviation | Mean | Standard Deviation | Mean | Standard Deviation | |
Beauty | 0.429 | 0.224 | 0.395 | 0.1845 | 0.389 | 0.2219 | 0.396 | 0.175 |
Boredom | 0.378 | 0.042 | 0.499 | 0.0842 | 0.359 | 0.026 | 0.431 | 0.063 |
Safety | 0.470 | 0.195 | 0.424 | 0.151 | 0.443 | 0.181 | 0.450 | 0.150 |
Liveliness | 0.724 | 0.082 | 0.489 | 0.160 | 0.771 | 0.025 | 0.641 | 0.123 |
Perceived Features | Beauty | Safety | Liveliness | Boredom |
---|---|---|---|---|
1. Green visual index (GVI) | 0.804 ** | 0.793 ** | 0.024 | −0.042 |
2. Vehicle occurrence rate (VOR) | 0.011 | −0.006 | 0.279 ** | −0.326 ** |
3. Natural to artificial ratio of the vertical interface (VI) | 0.345 ** | 0.362 ** | 0.701 ** | −0.742 ** |
4. Walkable streets (WS) | 0.003 | −0.032 | −0.024 | −0.04 |
5. Openness (OP) | −0.285 ** | −0.318 ** | −0.763 ** | 0.783 ** |
6. Pedestrians (PR) | 0.007 | 0.013 | 0.180 ** | −0.196 ** |
7. Building enclosure (BE) | −0.550 ** | −0.522 ** | 0.498 ** | −0.508 ** |
Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | VIF | Adjusted R Square | F-Sig. | ||
---|---|---|---|---|---|---|---|---|---|
B | Std. Error | Beta | |||||||
3 | (Constant) | −0.352 | 0.273 | −1.292 | 0.197 | 0.653 | 0.000 | ||
1. Green visual index (GVI) | 4.738 | 0.414 | 0.641 | 11.452 | 0 | 8.605 | |||
5. Openness (OP) | −2.106 | 0.504 | −0.177 | −4.179 | 0 | 4.946 | |||
7. Building enclosure (BE) | −1.281 | 0.471 | −0.16 | −2.718 | 0.007 | 9.504 | |||
Dependent Variable: Beauty |
Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | VIF | Adjusted R Square | F-Sig. | ||
---|---|---|---|---|---|---|---|---|---|
B | Std. Error | Beta | |||||||
3 | (Constant) | −0.105 | 0.277 | −0.38 | 0.704 | 0.643 | 0.000 | ||
1. Green visual index (GVI) | 4.382 | 0.419 | 0.593 | 10.447 | 0 | 8.605 | |||
5. Openness (OP) | −2.791 | 0.511 | −0.235 | −5.464 | 0 | 4.946 | |||
3. Natural to artificial ratio of the vertical interface (VI) | −1.523 | 0.478 | −0.19 | −3.187 | 0.001 | 9.504 | |||
Dependent Variable: Safety |
Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | VIF | Adjusted R Square | F-Sig. | ||
---|---|---|---|---|---|---|---|---|---|
B | Std. Error | Beta | |||||||
4 | (Constant) | 0.856 | 0.075 | 11.339 | 0 | 0.628 | 0.000 | ||
5. Openness (OP) | −7.805 | 0.261 | −0.657 | −29.944 | 0 | 1.236 | |||
7. Building enclosure (BE) | 1.727 | 0.176 | 0.216 | 9.783 | 0 | 1.246 | |||
2. Vehicle occurrence rate (VOR) | 0.153 | 0.064 | 0.05 | 2.389 | 0.017 | 1.111 | |||
6. Pedestrians (PR) | 10.746 | 5.361 | 0.04 | 2.004 | 0.045 | 1.035 | |||
Dependent Variable: Liveliness |
Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | VIF | Adjusted R Square | F-Sig. | ||
---|---|---|---|---|---|---|---|---|---|
B | Std. Error | Beta | |||||||
5 | (Constant) | 0.129 | 0.308 | 0.418 | 0.676 | 0.671 | 0.000 | ||
5. Openness (OP) | 6.185 | 0.59 | 0.521 | 10.489 | 0 | 7.155 | |||
7. Building enclosure (BE) | −1.677 | 0.166 | −0.209 | −10.101 | 0 | 1.247 | |||
2. Vehicle occurrence rate (VOR) | −0.3 | 0.06 | −0.097 | −4.968 | 0 | 1.112 | |||
3. Natural to artificial ratio of the vertical interface (VI) | −1.553 | 0.479 | −0.157 | −3.241 | 0.001 | 6.811 | |||
6. Pedestrians (PR) | −14.225 | 5.042 | −0.053 | −2.821 | 0.005 | 1.035 | |||
Dependent Variable: Boredom |
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Lu, X.; Li, Q.; Ji, X.; Sun, D.; Meng, Y.; Yu, Y.; Lyu, M. Impact of Streetscape Built Environment Characteristics on Human Perceptions Using Street View Imagery and Deep Learning: A Case Study of Changbai Island, Shenyang. Buildings 2025, 15, 1524. https://doi.org/10.3390/buildings15091524
Lu X, Li Q, Ji X, Sun D, Meng Y, Yu Y, Lyu M. Impact of Streetscape Built Environment Characteristics on Human Perceptions Using Street View Imagery and Deep Learning: A Case Study of Changbai Island, Shenyang. Buildings. 2025; 15(9):1524. https://doi.org/10.3390/buildings15091524
Chicago/Turabian StyleLu, Xu, Qingyu Li, Xiang Ji, Dong Sun, Yumeng Meng, Yiqing Yu, and Mei Lyu. 2025. "Impact of Streetscape Built Environment Characteristics on Human Perceptions Using Street View Imagery and Deep Learning: A Case Study of Changbai Island, Shenyang" Buildings 15, no. 9: 1524. https://doi.org/10.3390/buildings15091524
APA StyleLu, X., Li, Q., Ji, X., Sun, D., Meng, Y., Yu, Y., & Lyu, M. (2025). Impact of Streetscape Built Environment Characteristics on Human Perceptions Using Street View Imagery and Deep Learning: A Case Study of Changbai Island, Shenyang. Buildings, 15(9), 1524. https://doi.org/10.3390/buildings15091524