Visual Perception Optimization of Residential Landscape Spaces in Cold Regions Using Virtual Reality and Machine Learning
Abstract
:1. Introduction
- This research will reveal the correlation of inter-house landscape design factors with the landscape visual perception scores (W).
- In non-snow and snow seasons, the influence of the landscape design factors on inter-house landscape visual perception scores (W) will be illustrated in this paper.
- By GA combined with KNN, the thresholds of inter-house landscape factors are calculated, and the landscape optimization design method will be shown.
2. Materials and Methods
2.1. Study Area
2.2. SD Questionnaire
2.2.1. SD Questionnaire Focus
2.2.2. SD Questionnaire Settings
2.2.3. Participants of the SD Questionnaire Survey
2.3. Eye Tracker
2.3.1. Real Scene Eye Tracker
2.3.2. VR Eye Tracker
2.4. Orthogonal Experiment
2.4.1. Orthogonal Experimental Setup
2.4.2. Orthogonal Experiment Procedure
2.5. Machine Learning and Genetic Algorithms
2.5.1. Machine Learning
2.5.2. Genetic Algorithm
3. Results
3.1. Identification of Important Factors Influencing Visual Perception
3.1.1. SD Questionnaire Element Screening
3.1.2. Eye Tracker Interest Point Screening
3.1.3. VR Eye-Tracking Verification
3.2. Influence Mechanism
3.2.1. Influence Mechanism of Environmental Factors in Non-Snow Season
3.2.2. Influence Mechanism of Environmental Factors in Snow Season
3.3. Threshold Optimization
3.3.1. Machine Learning
3.3.2. Optimizing the Threshold
4. Discussion
4.1. Walking with a VR Headset Is More Accurate Than Using a Joystick Remote Control to Obtain Visual Focus
4.2. Machine Learning Model Comparison
4.3. Limitation of Subject Group Selection and Leisure Type
4.4. Limitations of Screening Leisure Visual Landscape Environmental Factors
4.5. Limitations of Research Application and Evaluation Monitoring
5. Conclusions
5.1. Landscape Environment Factors
5.2. Environmental Factors and Landscape Visual Perception Scores
5.3. Optimized Variable Ranges
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Appendix A.1. Decision Tree
Appendix A.2. Support Vector Machines (SVMs)
Appendix A.3. K-Nearest Neighbor (KNN)
Appendix A.4. Artificial Neural Network (ANN)
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Case | A | B | C | D |
---|---|---|---|---|
Construction time | 1990 | 2000 | 2013 | 2016 |
Floor area ratio | 2.5 | 2.1 | 2.1 | 2.5 |
Green space ratio | 24% | 20% | 30% | 30% |
Live photos (non-snow season) | ||||
Live photos (snow season) |
Number | Factors | Adjective Pairs |
---|---|---|
1 | Space aspect ratio | Narrow–wide |
2 | Roof height difference | Low–high |
3 | Openness of sky | Small–big |
4 | Distance from leisure landscape space to building | Near–far |
5 | Building orientation angle | Low–high |
6 | The proportion of grass in the field of view | Low–high |
7 | The height of tall trees | Low–high |
8 | Color of buildings—H | Cold–warm |
9 | Color of buildings—S | Low–high |
10 | Color of buildings—V | Darkness–brightness |
11 | Color of ground—H | Cold–warm |
12 | Color of ground—S | Low–high |
13 | Color of ground—V | Darkness–brightness |
14 | Color quantity | Little–much |
15 | Hue contrast in visual field | Small–large |
Environmental Factors | Orthogonal Experimental Parameters in the Non-Snow Season |
Space aspect ratio (SAR) | 1.2, 1.5, 1.8, 2.1, 2.4, 2.7, and 3 |
Roof height difference (RHD) | 0 m, 12 m, 24 m, 36 m, 48 m, 60 m, and 72 m |
Color saturation of buildings (BS) | 0, 20, 40, 60, 80, and 100 |
Tall-tree height (TTH) | 6 m, 10 m, 14 m, 18 m, 22 m, 26 m, and 30 m |
The proportion of grass in the field of view (GP) | 0, 3%, 6%, 9%, 12%, and 16% |
Hue contrast in the visual field (HC) | 0, 30°, 60°, 90°, 120°, 150°, and 180° |
Environmental Factors | Orthogonal Experimental Parameters in the Snow Season |
---|---|
Space aspect ratio (SAR) | 1.2, 1.5, 1.8, 2.1, 2.4, 2.7, and 3 |
Saturation of buildings (BS) | 0, 20, 40, 60, 80, and 100 |
The proportion of grass in the field of view (GP) | 0, 3%, 6%, 9%, 12%, and 16% |
Solution Set 1 | Solution Set 2 | Solution Set 3 | Solution Set 4 | … | Solution Set 30 | Range | |
---|---|---|---|---|---|---|---|
SAR | 1.8795 | 1.9057 | 1.821 | 1.9776 | … | 2.1485 | 1.82–2.15 |
RHD (m) | 15.094 | 10.8099 | 15.9183 | 18.1143 | … | 20.093 | 10.81–20.09 |
BS | 58.2669 | 60.2785 | 48.5304 | 52.8396 | … | 61.0109 | 48.53–61.01 |
TTH (m) | 17.5717 | 16.7644 | 14.3164 | 17.3361 | … | 18.2912 | 14.18–18.29 |
GP (%) | 0.125 | 0.1369 | 0.1185 | 0.146 | … | 0.154 | 0.12–0.15 |
HC | 19.9477 | 18.6411 | 20.6017 | 25.8764 | … | 26.8322 | 18.64–26.83 |
W (Predict) | 5 (4.90) | 5 (4.70) | 5 (4.85) | 5 (4.96) | … | 5 (4.84) | 5 (4.70–5.0) |
Solution Set 1 | Solution Set 2 | Solution Set 3 | Solution Set 4 | … | Solution Set 30 | Range | |
---|---|---|---|---|---|---|---|
SAR | 2.2234 | 2.4057 | 2.5211 | 2.3716 | … | 2.5425 | 2.22–2.54 |
BS | 68.5324 | 70.8215 | 78.5304 | 72.8396 | … | 82.3412 | 68.53–82.34 |
GP (%) | 0.104 | 0.139 | 0.115 | 0.136 | … | 0.144 | 0.1–0.14 |
W (Predict) | 5 (4.76) | 5 (4.80) | 5 (4.90) | 5 (4.67) | … | 5 (4.84) | 5 (4.67–4.90) |
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Li, X.; Huang, K.; Zhang, R.; Chen, Y.; Dong, Y. Visual Perception Optimization of Residential Landscape Spaces in Cold Regions Using Virtual Reality and Machine Learning. Land 2024, 13, 367. https://doi.org/10.3390/land13030367
Li X, Huang K, Zhang R, Chen Y, Dong Y. Visual Perception Optimization of Residential Landscape Spaces in Cold Regions Using Virtual Reality and Machine Learning. Land. 2024; 13(3):367. https://doi.org/10.3390/land13030367
Chicago/Turabian StyleLi, Xueshun, Kuntong Huang, Ruinan Zhang, Yang Chen, and Yu Dong. 2024. "Visual Perception Optimization of Residential Landscape Spaces in Cold Regions Using Virtual Reality and Machine Learning" Land 13, no. 3: 367. https://doi.org/10.3390/land13030367