This study proposes a seasonal machine learning model using crowdsourced data to explore differences in restorative quality between Spring and Summer at Jiangnan University. Using 800 street view images and the Perceived Restorativeness Scale-11 (PRS-11), we trained a Random Forest model to investigate the relationship between restoration and seasonal changes in campus landscapes. The results revealed four key findings: (a) spring campus landscapes are more conducive to students' mental restoration than summer landscapes; (b) waterfront areas exhibit significant seasonal differences in restorative quality, while teaching and sports zones show minimal variation; (c) positive landscape perceptions significantly enhance attention restoration quality, particularly in spring, whereas negative emotions correlate negatively with restoration in both seasons; and (d) artificial elements (e.g., buildings) and low-level landscape visual features serve as critical predictors of restorative quality. This study provides a seasonal perspective to examine the mental health benefits of campus landscapes.
AbrahamA.SommerhalderK.AbelT. (2010). Landscape and well-being: A scoping study on the health-promoting impact of outdoor environments. International Journal of Public Health, 55, 59–69.
2.
AlkaabiK.MehmoodK.Bin HdhaibaS. O.AljaberiS.AlkaabiN. (2024). Effect of thermal environmental factors on female students during summer and spring season: Promoting a sustainable campus initiative. Discover Sustainability, 5, 49.
3.
BielinisE.OmelanA.BoikoS.BielinisL. (2018). The restorative effect of staying in a broad-leaved forest on healthy young adults in winter and spring. Baltic Forestry, 24, 218–227.
BreimanL. (2001). Random forests. Machine learning, 45, 5–32.
6.
CaoY.HuangL. (2023). Research on the healing effect evaluation of campus’ small-scale courtyard based on the method of semantic differential and the perceived restorative scale. Sustainability, 15(10), 8369. https://doi.org/10.3390/su15108369
7.
CarlosV.GabrielaR.SoaresS. C. (2024). Active learning spaces design and assessment: a qualitative systematic literature review. Interactive Learning Environments, 32, 2925–2942.
8.
CelikorsE.WellsN. M. (2022). Are low-level visual features of scenes associated with perceived restorative qualities?Journal of Environmental Psychology, 81, 101800.
9.
ChenH.YeJ.-H. (2023). The influence of outdoor activities and campus landscape on university students’ subjective well-being during the COVID-19 pandemic. Sustainability, 15(5), 4157. https://doi.org/10.3390/su15054157
10.
ChengB.SchwingA.KirillovA. (2021). Per-pixel classification is not all you need for semantic segmentation. Advances in Neural Information Processing Systems, 34, 17864–17875.
11.
ChiangY.-C.LiuH.-H.LiD.HoL.-C. (2023). Quantification through deep learning of sky view factor and greenery on urban streets during hot and cool seasons. Landscape and Urban Planning, 232, 104679.
12.
CordtsM.OmranM.RamosS.RehfeldT.EnzweilerM.BenensonR.FrankeU.RothS.SchieleB. (2016). The cityscapes dataset for semantic urban scene understanding. Proceedings of the IEEE conference on computer vision and pattern recognition. Las Vegas, NV, USA.
13.
CrossanC.SalmoniA. (2021). A simulated walk in nature: Testing predictions from the attention restoration theory. Environment and Behavior, 53, 277–295.
14.
DongW.WuJ.ChenY.ZhouX. (2023). A bibliometric review of research on the perceptions of campus public spaces. Buildings, 13(2), 501. https://doi.org/10.3390/buildings13020501
15.
DongW.DaiD. H.ShenP. Y.ZhangR.LiuM. (2024). How public urban space enhance restoration benefits through combined multisensory effects: A systematic review. Land, 13(12), 1–17. https://doi.org/10.3390/land13122018
16.
DubeyA.NaikN.ParikhD.RaskarR.HidalgoC. A. (2016). Deep learning the city: Quantifying urban perception at a global scale. In LeibeB.MatasJ.SebeN.WellingM. (Eds.) Computer Vision – ECCV 2016, (pp. 196–212). Springer.
17.
FelstenG. (2009). Where to take a study break on the college campus: An attention restoration theory perspective. Journal of Environmental Psychology, 29, 160–167.
18.
FuW. J.GaoF.ZhangX.DongB.ChenX. L.XuX.YangZ. Y.LiuY. (2024). Tree–shrub–grass composite woodland better facilitates emotional recovery in college students emotion better than other plant communities. Frontiers in Psychology, 15, 1285792.
19.
GaoX.GengY.SpenglerJ. D.LongJ.LiuN.LuoZ.KalantariS.ZhuangW. (2025). Evaluating the impact of spatial openness on stress recovery: A virtual reality experiment study with psychological and physiological measurements. Building and Environment, 269, 112434.
20.
GaterslebenB.AndrewsM. (2013). When walking in nature is not restorative—The role of prospect and refuge. Health & Place, 20, 91–101.
21.
GomezT.DerrV. (2021). Landscapes as living laboratories for sustainable campus planning and stewardship: A scoping review of approaches and practices. Landscape and Urban Planning, 216, 104259.
22.
GuoW.WenH.LiuX. (2023). Research on the psychologically restorative effects of campus common spaces from the perspective of health. Frontiers in Public Health, 11, 1131180.
23.
HaJ.KimH. J. (2021). The restorative effects of campus landscape biodiversity: Assessing visual and auditory perceptions among university students. Urban Forestry & Urban Greening, 64, 127259.
24.
HamiA.AbdiB. (2021). Students' landscaping preferences for open spaces for their campus environment. Indoor and Built Environment, 30, 87–98.
25.
HartigT. (1996). Validation of a measure of perceived environmental restorativeness. Goteborg Psychological Reports, 26(7), 1–64.
26.
HastieT.TibshiraniR.FriedmanJ. H. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer.
27.
HeH.ZhangT.ZhangQ. H.RongS.JiaY. H.DongF. Q. (2024). Exploring the impact of campus landscape visual elements combination on short-term stress relief among college students: A case from china. Buildings, 14(5), 1340. https://doi.org/10.3390/buildings14051340
28.
HerzogT. R.ColleenMaguireP.NebelM. B. (2003). Assessing the restorative components of environments. Journal of Environmental Psychology, 23, 159–170.
29.
HongY.-K.ChoJ. Y. (2025). Characteristics of physical environments that enhance learning: A systematic review of EEG-Based empirical studies. Journal of Environmental Psychology, 102, 102525.
30.
HooybergA.MichelsN.AllaertJ.VandegehuchteM. B.EveraertG.De HenauwS.RooseH. (2022). ‘Blue’coasts: Unravelling the perceived restorativeness of coastal environments and the influence of their components. Landscape and Urban Planning, 228, 104551.
31.
HooybergA.MichelsN.RooseH.EveraertG.MokasI.MalinaR.VanderhasseltM.-A.De HenauwS. (2023). The psychophysiological reactivity to beaches vs. to green and urban environments: Insights from a virtual reality experiment. Journal of Environmental Psychology, 91, 102103.
32.
HuangK. T.WangT. Y.LiX. S.ZhangR. N.DongY. (2024). Predicting and optimizing restorativeness in campus pedestrian spaces based on vision using machine learning and deep learning. Land, 13(8), 1308. https://doi.org/10.3390/land13081308
33.
JanszS. N.Van DijkT.MobachM. P. (2020). Critical success factors for campus interaction spaces and services - a systematic literature review. Journal of Facilities Management, 18(2), 89–108.
34.
KanZ.KwanM.-P.CaiJ.LiuY.LiuD. (2023). Nonstationary relationships among individuals’ concurrent exposures to noise, air pollution and greenspace: A mobility-based study using GPS and mobile sensing data. Health & Place, 83, 103115.
35.
KangY.AbrahamJ.CeccatoV.DuarteF.GaoS.LjungqvistL.ZhangF.NäsmanP.RattiC. (2023). Assessing differences in safety perceptions using GeoAI and survey across neighbourhoods in Stockholm, Sweden. Landscape and Urban Planning, 236, 104768.
36.
KaplanR.KaplanS. (1989). The experience of nature: A psychological perspective. Cambridge university press.
37.
KaplanS. (1995). The restorative benefits of nature: Toward an integrative framework. Journal of Environmental Psychology, 15, 169–182.
38.
KaplanS.BermanM. G. (2010). Directed Attention as a Common Resource for Executive Functioning and Self-Regulation. Perspectives on Psychological Science, 5, 43–57.
39.
KaplanS.PetersonC. (1993). Health and environment: A psychological analysis. Landscape and Urban Planning, 26, 17–23.
40.
KexinS.LiZ.ZhengS.QuH. (2024). Quantifying environmental characteristics on psychophysiological restorative benefits of campus window views. Building and Environment, 262, 111822.
41.
KuperR. (2020). Preference and restorative potential for landscape models that depict diverse arrangements of defoliated, foliated, and evergreen plants. Urban Forestry & Urban Greening, 48, 126570.
42.
LeeK. E.WilliamsK. J. H.SargentL. D.WilliamsN. S. G.JohnsonK. A. (2015). 40-second green roof views sustain attention: The role of micro-breaks in attention restoration. Journal of Environmental Psychology, 42, 182–189.
43.
LiW.ZhaoZ.ChenD.PengY.LuZ. (2022). Prevalence and associated factors of depression and anxiety symptoms among college students: A systematic review and meta-analysis. Journal of Child Psychology and Psychiatry, 63, 1222–1230.
44.
LiJ.XuC.WanK.LiuY.LiuL. (2023). Mindfulness-based interventions to reduce anxiety among Chinese college students: A systematic review and meta-analysis. Frontiers in Psychology, 13, 1031398.
45.
LinW.ZengC. C.BaoZ. Y.JinH. X. (2023). The therapeutic look up: Stress reduction and attention restoration vary according to the sky-leaf-trunk (SLT) ratio in canopy landscapes. Landscape and Urban Planning, 234, 104730.
46.
LiuQ. Y.LuoS. J.ShenY. P.ZhuZ. P.YaoX.LiQ. Y.TarinM. W. K.ZhengJ. M.ZhuoZ. X. (2022). Relationships between students' demographic characteristics, perceived naturalness and patterns of use associated with campus green space, and self-rated restoration and health. Urban Forestry & Urban Greening, 68, 9.
47.
LiuR.AwadaM.Becerik GerberB.LucasG. M.RollS. C. (2023). Gender moderates the effects of ambient bergamot scent on stress restoration in offices. Journal of Environmental Psychology, 91, 102135.
48.
LiuY.ZhangJ.LiuC.YangY. (2024). A review of attention restoration theory: Implications for designing restorative environments. Sustainability, 16(9), 3639. https://doi.org/10.3390/su16093639
49.
LuM.FuJ. (2019). Attention restoration space on a university campus: Exploring restorative campus design based on environmental preferences of students. International Journal of Environmental Research and Public Health, 16, 2629.
50.
LuoS. X.ShiJ. Y.LuT. Y.FuruyaK. (2022). Sit down and rest: Use of virtual reality to evaluate preferences and mental restoration in urban park pavilions. Landscape and Urban Planning, 220, 104336.
51.
MaH.XuQ.ZhangY. (2023). High or low? Exploring the restorative effects of visual levels on campus spaces using machine learning and street view imagery. Urban Forestry & Urban Greening, 88, 128087.
52.
MaH.ZhangY.LiuP.ZhangF.ZhuP. (2024). How does spatial structure affect psychological restoration? A method based on graph neural networks and street view imagery. Landscape and Urban Planning, 251, 105171.
53.
MaH.LiJ.YeX. (2025a). Deep learning meets urban design: Assessing streetscape aesthetic and design quality through AI and cluster analysis. Cities, 162, 105939.
54.
MaH.ZhangY.CuiQ.KwanM.-P.CaiJ. (2025b). Perceived distance to greenery affects psychological restoration. Environment and Planning B: Urban Analytics and City Science. Advance online publication. https://doi.org/10.1177/23998083251362610
55.
MaX. D.ZhangZ. H.ChenY. M.ZhangW. Z. (2025c). How campus green space elements differently influence students' thermal perception during transition seasons?International Journal of Thermal Sciences, 212, 109767.
56.
MadanÖ. K.ChamilothoriK.Van DuijnhovenJ.AartsM. P.De KortY. A. (2024). Restorative effects of daylight in indoor environments–A systematic literature review. Journal of Environmental Psychology, 97, 102323.
57.
MenzelC.ReeseG. (2022). Seeing nature from low to high levels: Mechanisms underlying the restorative effects of viewing nature images. Journal of Environmental Psychology, 81, 101804.
58.
MollA.ColladoS.StaatsH.CorralizaJ. A. (2022). Restorative effects of exposure to nature on children and adolescents: A systematic review. Journal of Environmental Psychology, 84, 101884.
59.
OhlyH.WhiteM. P.WheelerB. W.BethelA.UkoumunneO. C.NikolaouV.GarsideR. (2016). Attention Restoration Theory: A systematic review of the attention restoration potential of exposure to natural environments. Journal of Toxicology and Environmental Health, Part B, 19, 305–343.
60.
PasiniM.BertoR.BrondinoM.HallR.OrtnerC. (2014). How to Measure the Restorative Quality of Environments: The PRS-11. Procedia - Social and Behavioral Sciences, 159, 293–297.
61.
QiY.ChenQ.LinF.LiuQ.ZhangX.GuoJ.QiuL.GaoT. (2022). Comparative study on birdsong and its multi-sensory combinational effects on physio-psychological restoration. Journal of Environmental Psychology, 83, 101879.
62.
QiZ.JinX.LiangX.HanB.ZhouY. (2024). A new framework for dynamic visual landscape quality evaluation of greenways based on perceived sensory dimensions: A case study of Chengdu urban greenway. Journal of Environmental Management, 370, 122950.
63.
RatcliffeE. (2021). Sound and Soundscape in Restorative Natural Environments: A Narrative Literature Review. Frontiers in Psychology, 12, 570563.
64.
RuddellE. J.HammittW. E. (1987). Prospect refuge theory - a psychological orientation for edge effect in recreation environments. Journal of Leisure Research, 19, 249–260.
65.
SchölkopfB.PlattJ.HofmannT. (2007). TrueSkill™: A Bayesian Skill Rating System. In PereiraF.BurgesC. J. C.BottouL.WeinbergerK. Q. (Eds.), Advances in neural information processing systems 20 (NIPS 2007) (pp. 569–576). Curran Associates, Inc.
66.
UlrichR. S.SimonsR. F.LositoB. D.FioritoE.MilesM. A.ZelsonM. (1991). Stress recovery during exposure to natural and urban environments. Journal of Environmental Psychology, 11, 201–230.
67.
UlrichR. S.BiophiliaB. (1993). Natural landscapes. In KellertS. E.WilsonE. O. (Eds.), The Biophilia Hypothesis (pp. 73–137). Island Press.
68.
VaswaniA.ShazeerN.ParmarN.UszkoreitJ.JonesL.GomezA. N.KaiserŁ.PolosukhinI. (2017). Attention is all you need. Advances in neural information processing systems, 30, 1–11.
69.
WangR. H.JiangW. X.LuT. S. (2021a). Landscape characteristics of university campus in relation to aesthetic quality and recreational preference. Urban Forestry & Urban Greening, 66, 127389.
70.
WangR. H.JiangW. X.LuT. S. (2021b). Landscape characteristics of university campus in relation to aesthetic quality and recreational preference. Urban Forestry & Urban Greening, 66, 9.
71.
WhiteM.SmithA.HumphryesK.PahlS.SnellingD.DepledgeM. (2010). Blue space: The importance of water for preference, affect, and restorativeness ratings of natural and built scenes. Journal of Environmental Psychology, 30, 482–493.
72.
XiangL.CaiM.RenC.NgE. (2021). Modeling pedestrian emotion in high-density cities using visual exposure and machine learning: Tracking real-time physiology and psychology in Hong Kong. Building and Environment, 205, 108273.
73.
YuanS.BrowningM.McanirlinO.SindelarK.ShinS.DrongG.HoptmanD.HellerW. (2023). A virtual reality investigation of factors influencing landscape preferences: Natural elements, emotions, and media creation. Landscape and Urban Planning, 230(5), 104616.
74.
ZhangF.ZhouB.LiuL.LiuY.FungH. H.LinH.RattiC. (2018). Measuring human perceptions of a large-scale urban region using machine learning. Landscape and Urban Planning, 180, 148–160.
75.
ZhangR.ZhuL.YangX.HanR.ZhangY.KangJ. (2024). The effects of interaction with audiovisual elements on perceived restoration in urban parks in freezing weather. Urban Forestry & Urban Greening, 96, 128337.
76.
ZhangJ.LiuS.LiuK.BianF. (2025). How does campus-scape influence university students' restorative experiences: Evidences from simultaneously collected physiological and psychological data. Urban Forestry & Urban Greening, 107, 128779.
77.
ZhouY.YangL.YuJ.GuoS. (2022). Do seasons matter? Exploring the dynamic link between blue-green space and mental restoration. Urban Forestry & Urban Greening, 73, 127612.
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