More trees, more restorativeness? Exploring the influence of campus pedestrian space on stress recovery using street view imagery

Abstract

Campus pedestrian environments play a critical role in alleviating student stress through environmental factors. According to Stress Recovery Theory (SRT), visual exposure to natural elements may trigger restorative psychological responses. However, previous studies have rarely compared multiple campuses or comprehensively examined the combined influence of diverse pedestrian-space characteristics. Meanwhile, traditional questionnaire-based assessments are difficult to implement systematically in highly stimulating environments. To address these gaps, we propose a scalable approach combining street-view imagery (SVI) and machine learning to evaluate the restorative quality of campus pedestrian environments. Across four Nanning campuses, we quantified visual features from 1060 street-view images, then paired these with on-site Restoration Outcome Scale (ROS) scores. Finally, multiple regression and random forest models were used to assess linear and nonlinear links to perceived restorativeness, achieving high accuracy (RMSE = 0.078; MSE = 0.049; R² = 0.602). Key findings are threefold. First, campus restorative quality is systematically tied to a distinctive visual signature, with pronounced heterogeneity across universities. Second, while linear regression confirmed the positive influence of natural elements, the more robust machine-learning model identified vegetation cover and sky visibility as the two most important predictors. Third, partial-dependence analysis uncovered a nonlinear pattern: sky visibility consistently enhanced restorative quality, whereas vegetation’s effect became negative beyond a threshold. This challenges the conventional “more-is-better” assumption for campus greening and indicates that spatial configuration and perceived quality of environmental features, rather than sheer quantity, are more decisive for stress recovery. This provides a scalable, empirical method to optimize campus greenspace for student well-being.

Keywords

Restoration / Street-view imagery / Computer vision / Walkable campus environments / Stress recovery

References

[1] Anguelov D, Dulong C, Filip D, Frueh C, Lafon S, Lyon R, Ogale A, Vincent L, Weaver J. Google street view: capturing the world at street level. Computer, 2010, 43: 32-38

[2] Berto R. The role of nature in coping with psycho-physiological stress: a literature review on restorativeness. Behavioral Science, 2014, 4: 394-409

[3] Biljecki F, Ito K. Street view imagery in urban analytics and GIS: a review. Landsc Urban Plan, 2021, 215: 104217

[4] Browning MHEM, Rigolon A. School green space and its impact on academic performance: a systematic literature review. International Journal Of Environmental Research And Public Health, 2019, 16: 429

[5] Chen X, Meng Q, Hu D, Zhang L, Yang J. Evaluating greenery around streets using baidu panoramic street view images and the panoramic green view index. Forests, 2019, 10: 1109

[6] Cheng X, Liu J, Liu H, Lu S. A systematic review of evidence of additional health benefits from forest exposure. Landsc Urban Plan, 2021, 212: 104123

[7] Chrisinger, B. W., & King, A. C. (2018). Stress experiences in neighborhood and social environments (SENSE): a pilot study to integrate the quantified self with citizen science to improve the built environment and health. International Journal Of Health Geographics, 17. https://doi.org/10.1186/s12942-018-0140-1

[8] Connolly K. Multisensory perception as an associative learning process. Frontiers In Psychology, 2014

[9] Dosen AS, Ostwald MJ. Evidence for prospect-refuge theory: a meta-analysis of the findings of environmental preference research. City Territ Archit, 2016, 3: 4

[10] Eisenberg D, Gollust SE, Golberstein E, Hefner JL. Prevalence and correlates of depression, anxiety, and suicidality among university students. American Journal Of Orthopsychiatry, 2007, 77: 534-542

[11] Ewert A, Chang Y. Levels of nature and stress response. Behavioral Science, 2018, 8: 49

[12] Gaekwad JS, Sal Moslehian A, Roös PB. A meta-analysis of physiological stress responses to natural environments: biophilia and stress recovery theory perspectives. Journal Of Environmental Psychology, 2023, 90: 102085

[13] Hadavi S, Kaplan R, Hunter MCR. Environmental affordances: a practical approach for design of nearby outdoor settings in urban residential areas. Landsc Urban Plan, 2015, 134: 19-32

[14] Han X, Wang L, He J, Jung T. Restorative perception of urban streets: interpretation using deep learning and MGWR models. Front Public Health 11, 2023

[15] Hartig T, Korpela K, Evans GW, Gärling T. A measure of restorative quality in environments. Scand Hous Plan Res, 1997, 14: 175-194

[16] Helbich M, Yao Y, Liu Y, Zhang J, Liu P, Wang R. Using deep learning to examine street view green and blue spaces and their associations with geriatric depression in beijing, china. Environment International, 2019, 126: 107-117

[17] Hodson CB, Sander HA. Green urban landscapes and school-level academic performance. Landsc Urban Plan, 2017, 160: 16-27

[18] Kaplan S. A model of person-environment compatibility. Environ Behav, 1983, 15: 311-332

[19] Kaplan S, Berman MG. Directed attention as a common resource for executive functioning and self-regulation. Perspect Psychol Sci, 2017

[20] Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W. Y., Dollár, P., & Girshick, R. (2023). Segment anything, in: 2023 IEEE/CVF International Conference on Computer Vision (ICCV). Presented at the 2023 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE, Paris, France. https://doi.org/10.1109/iccv51070.2023.00371

[21] Korpela KM, Ylén M, Tyrväinen L, Silvennoinen H. Determinants of restorative experiences in everyday favorite places. Health & Place, 2008, 14: 636-652

[22] Kotsiantis SB. Decision trees: a recent overview. Artificial Intelligence Review, 2013, 39: 261-283

[23] Larkin A, Hystad P. Evaluating street view exposure measures of visible green space for health research. Journal Of Exposure Science & Environmental Epidemiology, 2019, 29: 447-456

[24] Lau SSY, Gou Z, Liu Y. Healthy campus by open space design: approaches and guidelines. Front Archit Res, 2014, 3: 452-467

[25] Lee, J., Tsunetsugu, Y., Takayama, N., Park, B. J., Li, Q., Song, C., Komatsu, M., Ikei, H., Tyrväinen, L., Kagawa, T., & Miyazaki, Y. (2014). Influence of forest therapy on cardiovascular relaxation in young adults. Evid Based Complement Alternat Med, 2014. https://doi.org/10.1155/2014/834360

[26] Li W, Liu Y. The influence of visual and auditory environments in parks on visitors’ landscape preference, emotional state, and perceived restorativeness. Humanit Soc Sci Commun, 2024, 11: 1491

[27] Li D, Sullivan WC. Impact of views to school landscapes on recovery from stress and mental fatigue. Landsc Urban Plan, 2016, 148: 149-158

[28] Liu P, Zhao T, Luo J, Lei B, Frei M, Miller C, Biljecki F. Towards human-centric digital twins: leveraging computer vision and graph models to predict outdoor comfort. Sustain Cities Soc, 2023, 93: 104480

[29] Liu B, Kralj A, Moyle B, He M, Li Y. Perceived destination restorative qualities in wellness tourism: the role of ontological security and psychological resilience. J Travel Res, 2025, 64: 835-852

[30] Loo BP, Mahendran R, Katagiri K, Lam WW. Walking, neighbourhood environment and quality of life among older people. Current Opinion In Environmental Sustainability, 2017, 25: 8-13

[31] Luo S, Xie J, Wang H, Wang Q, Chen J, Yang Z, Furuya K. Natural dose of blue restoration: a field experiment on mental restoration of urban blue spaces. Land, 2023, 12: 1834

[32] Ma H, Xu Q, Zhang Y. High or low? Exploring the restorative effects of visual levels on campus spaces using machine learning and street view imagery. Urban For Urban Green, 2023, 88: 128087

[33] Ma Y, Zhang J, Yang X. Effects of audio-visual environmental factors on emotion perception of campus walking spaces in northeastern china. Sustainability, 2023, 15: 15105

[34] Ma H, Li J, Ye X. Deep learning meets urban design: assessing streetscape aesthetic and design quality through AI and cluster analysis. Cities, 2025, 162: 105939

[35] Markevych I, Schoierer J, Hartig T, Chudnovsky A, Hystad P, Dzhambov AM, De Vries S, Triguero-Mas M, Brauer M, Nieuwenhuijsen MJ, Lupp G, Richardson EA, Astell-Burt T, Dimitrova D, Feng X, Sadeh M, Standl M, Heinrich J, Fuertes E. Exploring pathways linking greenspace to health: theoretical and methodological guidance. Environmental Research, 2017, 158: 301-317

[36] Nordh H, Hartig T, Hagerhall CM, Fry G. Components of small urban parks that predict the possibility for restoration. Urban For Urban Green, 2009, 8: 225-235

[37] Pasanen TP, Tyrväinen L, Korpela KM. The relationship between perceived health and physical activity indoors, outdoors in built environments, and outdoors in nature. Appl Psychol Health Well-Being, 2014, 6: 324-346

[38] Pasanen TP, White MP, Elliott LR, Van Den Bosch M, Bratman GN, Ojala A, Korpela K, Fleming LE. Urban green space and mental health among people living alone: the mediating roles of relational and collective restoration in an 18-country sample. Environmental Research, 2023, 232: 116324

[39] Ratcliffe, E. (2021). Sound and soundscape in restorative natural environments: a narrative literature review. Frontiers In Psychology, 12. https://doi.org/10.3389/fpsyg.2021.570563

[40] Rzotkiewicz A, Pearson AL, Dougherty BV, Shortridge A, Wilson N. Systematic review of the use of Google street view in health research: major themes, strengths, weaknesses and possibilities for future research. Health & Place, 2018, 52: 240-246

[41] Schertz KE, Berman MG. Understanding nature and its cognitive benefits. Current Directions In Psychological Science, 2019, 28: 496-502

[42] Schoppa L, Disse M, Bachmair S. Evaluating the performance of random forest for large-scale flood discharge simulation. Journal Of Hydrology, 2020, 590: 125531

[43] Shi T, Horvath S. Unsupervised learning with random forest predictors. J Comput Graph Stat, 2006, 15: 118-138

[44] Southworth M. Designing the walkable city. Journal Of Urban Planning And Development, 2005, 131: 246-257 2005)131:4(246)

[45] Speiser JL, Miller ME, Tooze J, Ip E. A comparison of random forest variable selection methods for classification prediction modeling. Expert Systems With Applications, 2019, 134: 93-101

[46] Tabrizian P, Baran PK, Smith WR, Meentemeyer RK. Exploring perceived restoration potential of urban green enclosure through immersive virtual environments. Journal Of Environmental Psychology, 2018, 55: 99-109

[47] Takayama N, Korpela K, Lee J, Morikawa T, Tsunetsugu Y, Park BJ, Li Q, Tyrväinen L, Miyazaki Y, Kagawa T. Emotional, restorative and vitalizing effects of forest and urban environments at four sites in Japan. International Journal Of Environmental Research And Public Health, 2014, 11: 7207-7230

[48] Taylor S. Anxiety disorders, climate change, and the challenges ahead: introduction to the special issue. Journal Of Anxiety Disorders, 2020, 76: 102313

[49] Tyrväinen L, Ojala A, Korpela K, Lanki T, Tsunetsugu Y, Kagawa T. The influence of urban green environments on stress relief measures: a field experiment. Journal Of Environmental Psychology, 2014, 38: 1-9

[50] Ulrich RS, Simons RF, Losito BD, Fiorito E, Miles MA, Zelson M. Stress recovery during exposure to natural and urban environments. Journal Of Environmental Psychology, 1991, 11: 201-230

[51] Van Den Bogerd N, Dijkstra SC, Seidell JC, Maas J. Greenery in the university environment: students’ preferences and perceived restoration likelihood. PLOS One, 2018, 13: e0192429

[52] White M, Smith A, Humphryes K, Pahl S, Snelling D, Depledge M. Blue space: the importance of water for preference, affect, and restorativeness ratings of natural and built scenes. Journal Of Environmental Psychology, 2010, 30: 482-493

[53] Yang S, Zhang L, Liu S, Lu H, Chen H. Real-time semantic segmentation via a densely aggregated bilateral network. IEEE Trans Neural Netw Learn Syst, 2025, 36: 381-392

[54] Yao W, Zhang X, Gong Q. The effect of exposure to the natural environment on stress reduction: a meta-analysis. Urban For. Urban Green, 2021, 57: 126932

[55] Zhang F, Zhou B, Liu L, Liu Y, Fung HH, Lin H, Ratti C. Measuring human perceptions of a large-scale urban region using machine learning. Landsc Urban Plan, 2018, 180: 148-160

[56] Zhang J, Fukuda T, Yabuki N. Development of a city-scale approach for façade color measurement with building functional classification using deep learning and street view images. ISPRS Int J Geo-Inf, 2021, 10: 551

[57] Zhao T, Liang X, Tu W, Huang Z, Biljecki F. Sensing urban soundscapes from street view imagery. Computers, Environment And Urban Systems, 2023, 99: 101915

[58] Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A. (2017). Scene parsing through ADE20K dataset, in: 2017 IEEE Conference on Computer Vision and, Recognition, P., & Recognition, P. (CVPR), IEEE, Honolulu, HI, pp. 5122–5130. https://doi.org/10.1109/CVPR.2017.544

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