Measuring tourists’ visual perception of gardens around Taihu Lake Rim area based on multi-source data
Abstract
Tourists’ visual preferences are of paramount importance for the local garden environment assessment. However, the diversity of garden elements presents challenges in achieving uniform assessments. This study focuses on 65 modern gardens around Taihu Lake (太湖), utilizing image semantic segmentation and the Semantic Differential (SD) method to evaluate tourists’ visual perceptions, identifying 16 perceptual indicators associated with garden elements. The research findings indicate the following: (1) Modern gardens in different cities (Wuxi, Suzhou, Huzhou) offer distinct visual experiences to tourists. (2) Through quantitative analysis of garden elements and tourists’ visual perceptions, it is revealed that middle and high-rise vegetation, hydrology, architecture, and sketch elements enhance visual aesthetics, while main road and low-rise vegetation elements result in less pronounced perceptions. This study quantitatively explores the complexities in evaluating garden aesthetics and serves as a bridge between qualitative and quantitative aspects for future garden environmental impact assessments.
Keyword : garden environment, visual perception, image, element, tourists, environmental impact assessment
How to Cite
Liu, W., Zhang, R., & Li, H. (2024). Measuring tourists’ visual perception of gardens around Taihu Lake Rim area based on multi-source data. Journal of Environmental Engineering and Landscape Management, 32(2), 152–168. https://doi.org/10.3846/jeelm.2024.20972
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Acar, C., Kurdoglu, B. C., Kurdoglu, O., & Acar, H. (2006). Public preferences for visual quality and management in the Kackar Mountains National Park (Turkey). International Journal of Sustainable Development & World Ecology, 13(6), 499–512. https://doi.org/10.1080/13504500609469699
Agnew, J. A. (2011). The SAGE handbook of geographical knowledge. SAGE Publications Ltd. https://doi.org/10.4135/9781446201091
Bell, S. (2012). Landscape: Pattern, perception and process (2nd ed.). E and FN Spoon. https://doi.org/10.4324/9780203120088
Brown, T., Keane, T., & Kaplan, S. (1986). Aesthetics and management: Bridging the gap. Landscape and Urban Planning, 13, 1–10. https://doi.org/10.1016/0169-2046(86)90002-2
Cao, H., Yang, H., Jiang, W., Jianling, Y. E., & Dafang, H. E. (2019). The geological environment suitability assessment of underground space development in Changsha city. China Mining Magazine.
Cao, J., Liang, Y., & Zhang, J. (2004). The scenic investigation and assessment of nature reserves in Beijing. Chinese Landscape Architecture, 7, 67–71.
Cao, J., Wang, J., Wu, X., Ding, C., Wang, W., & Wang, H. (2020). Post-evaluation of urban river open space landscape restoration: A case study of the eastern part of the Inner Qinhuai River in Nanjing. Journal of Nanjing Forestry University, 44(3), 195–201.
Chen, R., Zhao, J., Hao, H., & Wang, K. (2021). A large-scale measurement method of esthetical appreciation laws based on the multimodal machine learning. ZHUANGSHI, 7, 106–111. https://doi.org/10.16272/j.cnki.cn11-1392/j.2021.07.022
Coeterier, J. (1996). Dominant attributes in the perception and evaluation of the Dutch landscape. Landscape and Urban Planning, 34(1), 27–44. https://doi.org/10.1016/0169-2046(95)00204-9
Cong, X., Yin, M., Ding, S., Wang, x., & Zhang, Q. (2021). A study on the perception and evaluation of the popularity of scenic spots of Chinese classical Gardens and the way of dissemination from the perspective of tourists: A case study of the Lingering Garden of Classical Gardens in Suzhou. Chinese Landscape Architecture, 37(08), 56–61.
Dai, L., Zheng, C., Dong, Z., Yao, Y., Wang, R., Zhang, X., Ren, S., Zhang, J., Song, X., & Guan, Q. (2021). Analyzing the correlation between visual space and residents’ psychology in Wuhan, China using street-view images and deep-learning technique. City and Environment Interactions, 11, Article 100069. https://doi.org/10.1016/j.cacint.2021.100069
Daniel, T. C. (2001). Whither scenic beauty? Visual landscape quality assessment in the 21st century. Landscape and Urban Planning, 54(1–4), 267–281. https://doi.org/10.1016/S0169-2046(01)00141-4
de Val, G. d. l. F., Atauri, J. A., & de Lucio, J. V. (2006). Relationship between landscape visual attributes and spatial pattern indices: A test study in Mediterranean-climate landscapes. Landscape and Urban Planning, 77(4), 393–407. https://doi.org/10.1016/j.landurbplan.2005.05.003
Dearnley, C. (2005). A reflection on the use of semi-structured interviews. Nurse Researcher, 13(1). https://doi.org/10.7748/nr2005.07.13.1.19.c5997
Dubey, A., Naik, N., Parikh, D., Raskar, R., & Hidalgo, C. A. (2016, October 11–14). Deep learning the city: Quantifying urban perception at a global scale [Paper presentation]. Computer Vision – ECCV 2016: Proceedings of the 14th European Conference, Amsterdam, The Netherlands. Springer. https://doi.org/10.1007/978-3-319-46448-0_12
Fletcher, R., Baulcomb, C., Hall, C., & Hussain, S. (2014). Revealing marine cultural ecosystem services in the Black Sea. Marine Policy, 50, 151–161. https://doi.org/10.1016/j.marpol.2014.05.001
Gebru, T., Krause, J., Wang, Y., Chen, D., Deng, J., Aiden, E. L., & Fei-Fei, L. (2017). Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States. Proceedings of the National Academy of Sciences, 114, 13108–13113. https://doi.org/10.1073/pnas.1700035114
Godbey, G., Mowen, A., & Ashburn, V. (2010). The benefits of physical activity provided by park and recreation services: The scientific evidence. National Recreation and Park Association Ashburn, VA.
Goodchild, M. F. (2010). Formalizing place in geographic information systems. In Communities, neighborhoods, and health: Expanding the boundaries of place (pp. 21–33). Springer. https://doi.org/10.1007/978-1-4419-7482-2_2
Gozalo, G. R., Morillas, J. M. B., González, D. M., & Moraga, P. A. (2018). Relationships among satisfaction, noise perception, and use of urban green spaces. Science of the Total Environment, 624, 438–450. https://doi.org/10.1016/j.scitotenv.2017.12.148
Guang, G. (2020). Research on evaluation of roof greening landscape based on visual perception. Huaqiao University.
Hadavi, S. (2017). Direct and indirect effects of the physical aspects of the environment on mental well-being. Environment and Behavior, 49(10), 1071–1104. https://doi.org/10.1177/0013916516679876
Hägerhäll, C. M., Ode Sang, Å., Englund, J.-E., Ahlner, F., Rybka, K., Huber, J., & Burenhult, N. (2018). Do humans really prefer semi-open natural landscapes? A cross-cultural reappraisal. Frontiers in Psychology, 9, Article 822. https://doi.org/10.3389/fpsyg.2018.00822
Han, G., & Zhu, Y. (2021). Analysis of application and practice essentials of landscape planning and design in leisure agriculture park–Taking Jiutai Ecotourism Resort in Fujian Province as an example. Journal of Jilin Agricultural Science and Technology University, 30(01), 20–22+32.
Hand, K. L., Freeman, C., Seddon, P. J., Recio, M. R., Stein, A., & Van Heezik, Y. (2017). The importance of urban gardens in supporting children’s biophilia. Proceedings of the National Academy of Sciences, 114(2), 274–279. https://doi.org/10.1073/pnas.1609588114
Helbich, M., Yao, Y., Liu, Y., Zhang, J., Liu, P., & Wang, R. (2019). Using deep learning to examine street view green and blue spaces and their associations with geriatric depression in Beijing, China. Environment International, 126, 107–117. https://doi.org/10.1016/j.envint.2019.02.013
Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A.-r., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T. N., & Kingsbury, B. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine, 29(6), 82–97. https://doi.org/10.1109/MSP.2012.2205597
Irvine, K. N., Warber, S. L., Devine-Wright, P., & Gaston, K. J. (2013). Understanding urban green space as a health resource: A qualitative comparison of visit motivation and derived effects among park users in Sheffield, UK. International Journal of Environmental Research and Public Health, 10(1), 417–442. https://doi.org/10.3390/ijerph10010417
Jiao, M., Gao, F., Hao, P., & Dong, L. (2013). Evaluation of urban banded park plant landscape based on SD method. Journal of Northwest Forestry College, 28(5), 185–190. https://doi.org/10.3969/j.issn.1001-7461.2013.05.37
Joly, D., Brossard, T., Cavailhès, J., Hilal, M., Tourneux, F.-P., Tritz, C., & Wavresky, P. (2009). A quantitative approach to the visual evaluation of landscape. Annals of the Association of American Geographers, 99(2), 292–308. https://doi.org/10.1080/00045600802708473
Kabisch, N., Qureshi, S., & Haase, D. (2015). Human–environment interactions in urban green spaces–A systematic review of contemporary issues and prospects for future research. Environmental Impact Assessment Review, 50, 25–34. https://doi.org/10.1016/j.eiar.2014.08.007
Kaplan, R., & Kaplan, S. (1989). The experience of nature: A psychological perspective. Cambridge University Press.
Kaplan, S. (1987). Aesthetics, affect, and cognition: Environmental preference from an evolutionary perspective. Environment and Behavior, 19(1), 3–32. https://doi.org/10.1177/0013916587191001
Kruse, J., Kang, Y., Liu, Y. N., Zhang, F., & Gao, S. (2021). Places for play: Understanding human perception of playability in cities using street view images and deep learning. Computers, Environment and Urban Systems, 90, Article 101693. https://doi.org/10.1016/j.compenvurbsys.2021.101693
Laaksonen, P., Laaksonen, M., Borisov, P., & Halkoaho, J. (2006). Measuring image of a city: A qualitative approach with case example. Place Branding, 2, 210–219. https://doi.org/10.1057/palgrave.pb.5990058
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Lei, F. (2020). Evaluation on garden plants landscape based on SD method–A Case in Xiangshihu Campus of Guangxi University of Finance and Economics. Journal of Shandong Agricultural University (Natural Science Edition), 51(5), 858–862. https://doi.org/10.3969/j.issn.1000-2324.2020.05.015
Li, C., Song, Y., Kaza, N., & Burghardt, R. (2023). Explaining spatial variations in residential energy usage intensity in Chicago: The role of urban form and geomorphometry. Journal of Planning Education and Research, 43(2), 317–331. https://doi.org/10.1177/0739456X19873382
Li, X., Ratti, C., & Seiferling, I. (2018). Quantifying the shade provision of street trees in urban landscape: A case study in Boston, USA, using Google Street View. Landscape and Urban Planning, 169, 81–91. https://doi.org/10.1016/j.landurbplan.2017.08.011
Liang, X., Luo, C., & Quan, Y. (2020). Research on progress of image semantic segmentation Based on deep learning. Computer Engineering and Applications, 56(2), 18–28.
Lifang, Q., Yichuan, Z., & Wei, C. (2008). Evaluation of urban river landscape design rationality based on AHP. Water Science and Engineering, 1(4), 75–81.
Lin, Y., Li, J., Li, L., Fu, W., & Dong, J. (2022). Landscape quality evaluation of urban canopy forest trail based on image semantic segmentation: A case study of Fudao in Fuzhou. Safety and Environmental Engineering, 29(03), 218–224+243. https://doi.org/10.13578/j.cnki.issn.1671-1556.20211187
Liu, M., Han, L., Xiong, S., Qing, L., Ji, H., & Peng, Y. (2019, August 23–25). Large-scale street space quality evaluation based on deep learning over street view image [Paper presentation]. Proceedings of the 10th International Conference on Image and Graphics, ICIG 2019, Beijing, China. https://doi.org/10.1007/978-3-030-34110-7_58
Liu, Y., & Xu, F. (2011). Scenic assessment in the districts of economically affordable housing. Journal of Northwest Forestry University, 26(6), 199–203.
Lothian, A. (1999). Landscape and the philosophy of aesthetics: Is landscape quality inherent in the landscape or in the eye of the beholder? Landscape and Urban Planning, 44(4), 177–198. https://doi.org/10.1016/S0169-2046(99)00019-5
Luo, J., Zhao, T., Cao, L., & Biljecki, F. (2022). Semantic Riverscapes: Perception and evaluation of linear landscapes from oblique imagery using computer vision. Landscape and Urban Planning, 228, Article 104569. https://doi.org/10.1016/j.landurbplan.2022.104569
Lynch, K. (1960). The image of the environment. The Image of the City, 11, 1–13.
Mitchell, R. (2013). Is physical activity in natural environments better for mental health than physical activity in other environments? Social Science & Medicine, 91, 130–134. https://doi.org/10.1016/j.socscimed.2012.04.012
Naik, N., Philipoom, J., Raskar, R., & Hidalgo, C. (2014). Streetscore -- Predicting the perceived safety of one million streetscapes [Paper presentation]. IEEE Conference on Computer Vision & Pattern Recognition Workshops, Columbus, OH, USA. https://doi.org/10.1109/CVPRW.2014.121
Nasar, J. L. (1990). The evaluative image of the city. Journal of the American Planning Association, 56(1), 41–53. https://doi.org/10.1177/0739456X9901800312
Ortolano, L. (1984). Environmental planning and decision making. United States. https://www.osti.gov/biblio/5734691
Osgood, C. E., Suci, G. J., & Tannenbaum, P. H. (1957). The measurement of meaning. University of Illinois Press.
Peng, X., & Huang, Z. (2017). A novel popular tourist attraction discovering approach based on geo-tagged social media big data. ISPRS International Journal of Geo-Information, 6(7), Article 216. https://doi.org/10.3390/ijgi6070216
Peng, X., Bao, Y., & Huang, Z. (2020). Perceiving Beijing’s “city image” across different groups based on geotagged social media data. IEEE Access, 8, 93868–93881. https://doi.org/10.1109/ACCESS.2020.2995066
Qiu, H., Che, S., Xie, C., & Pan, H. (2021). Public cognition of Shanghai city streetscape and landscape aesthetics based on deep learning. Chinese Landscape Architecture, 37(06), 77–81. https://doi.org/10.19775/j.cla.2021.06.0077
Qureshi, S., Breuste, J. H., & Lindley, S. J. (2010a). Green space functionality along an urban gradient in Karachi, Pakistan: A socio-ecological study. Human Ecology, 38, 283–294. https://doi.org/10.1007/s10745-010-9303-9
Qureshi, S., Kazmi, S. J. H., & Breuste, J. H. (2010b). Ecological disturbances due to high cutback in the green infrastructure of Karachi: Analyses of public perception about associated health problems. Urban Forestry & Urban Greening, 9(3), 187–198. https://doi.org/10.1016/j.ufug.2009.08.003
Rattenbury, T., & Naaman, M. (2009). Methods for extracting place semantics from Flickr tags. ACM Transactions on the Web, 3(1), Article 1. https://doi.org/10.1145/1462148.1462149
Richardson, E. A., Pearce, J., Mitchell, R., & Kingham, S. (2013). Role of physical activity in the relationship between urban green space and health. Public Health, 127(4), 318–324. https://doi.org/10.1016/j.puhe.2013.01.004
Routledge, B. P. (1993). Terrains of resistance. Praeger. https://doi.org/10.1016/0962-6298(96)00029-7
Salesses, P., Schechtner, K., & Hidalgo, C. A. (2013). The collaborative image of the city: Mapping the inequality of urban perception. PloS One, 8(7), Article e68400. https://doi.org/10.1371/journal.pone.0068400
Song, S., Xu, Y., Wu, Z., Deng, X., & Wang, Q. (2019). The relative impact of urbanization and precipitation on long-term water level variations in the Yangtze River Delta. Science of the Total Environment, 648, 460–471. https://doi.org/10.1016/j.scitotenv.2018.07.433
Tang, Z., & Liu, B. (2015). Progress in visual landscape evaluation. Landscape Architecture, 9, 113–120.
Torralba, A., Russell, B. C., & Yuen, J. (2010). LabelMe: Online image annotation and applications. Proceedings of the IEEE, 98(8), 1467–1484. https://doi.org/10.1109/JPROC.2010.2050290
Tuan, Y.-F. (1977). Space and place: The perspective of experience. University of Minnesota Press.
Tzoulas, K., Korpela, K., Venn, S., Yli-Pelkonen, V., Kaźmierczak, A., Niemela, J., & James, P. (2007). Promoting ecosystem and human health in urban areas using Green Infrastructure: A literature review. Landscape and Urban Planning, 81(3), 167–178. https://doi.org/10.1016/j.landurbplan.2007.02.001
Wan, C., Shen, G. Q., & Choi, S. (2020). Effects of physical and psychological factors on users’ attitudes, use patterns, and perceived benefits toward urban parks. Urban Forestry & Urban Greening, 51, Article 126691. https://doi.org/10.1016/j.ufug.2020.126691
Wei, J., Yue, W., Li, M., & Gao, J. (2022). Mapping human perception of urban landscape from street-view images: A deep-learning approach. International Journal of Applied Earth Observation and Geoinformation, 112, Article 102886. https://doi.org/10.1016/j.jag.2022.102886
Wendel, H. E. W., Zarger, R. K., & Mihelcic, J. R. (2012). Accessibility and usability: Green space preferences, perceptions, and barriers in a rapidly urbanizing city in Latin America. Landscape and Urban Planning, 107(3), 272–282. https://doi.org/10.1016/j.landurbplan.2012.06.003
Wu, X., Huang, Z., Peng, X., Chen, Y., & Liu, Y. (2018). Building a spatially-embedded network of tourism hotspots from geotagged social media data. IEEE Access, 6, 21945–21955. https://doi.org/10.1109/ACCESS.2018.2828032
Xi, Z., Li, C., Zhou, L., Yang, H., & Burghardt, R. (2023). Built environment influences on urban climate resilience: Evidence from extreme heat events in Macau. Science of the Total Environment, 859, Article 160270. https://doi.org/10.1016/j.scitotenv.2022.160270
Xu, Y., Zhang, Z., Yu, D., Yuan, D., & Li, Q. (2022). Semantic segmentation algorithm of dyke scene image based on attention guidance. Information Technology, (05), 88–93+100. https://doi.org/10.13274/j.cnki.hdzj.2022.05.015
Yang, H.-j., Zhu, T.-c., & Maruyama, J. (2004). Research on quantitative evaluation of visual effect of grassland landscapes–with grassland in construction in Sihori, Japan as an example. Acta Prataculturae Sinica, 13(4), 106–111.
Yao, Y., Liang, Z., Yuan, Z., Liu, P., Bie, Y., Zhang, J., Wang, R., Wang, J., & Guan, Q. (2019). A human-machine adversarial scoring framework for urban perception assessment using street-view images. International Journal of Geographical Information Science, 33(12), 2363–2384. https://doi.org/10.1080/13658816.2019.1643024
Yu, K. (1987). A preliminary study on China’s natural wind resources management system. Chinese Garden, (03), 33–37. https://xueshu.baidu.com/usercenter/paper/show?paperid=7b9c09e9f982ddd08a7f62f9477a393c&site=xueshu_se&hitarticle=1
Yuen, H. K., & Jenkins, G. R. (2020). Factors associated with changes in subjective well-being immediately after urban park visit. International Journal of Environmental Health Research, 30(2), 134–145. https://doi.org/10.1080/09603123.2019.1577368
Zhang, F., Zhang, D., Liu, Y., & Lin, H. (2018a). Representing place locales using scene elements. Computers, Environment and Urban Systems, 71, 153–164. https://doi.org/10.1016/j.compenvurbsys.2018.05.005
Zhang, F., Zhou, B., Liu, L., Liu, Y., Fung, H. H., Lin, H., & Ratti, C. (2018b). Measuring human perceptions of a large-scale urban region using machine learning. Landscape and Urban Planning, 180, 148–160. https://doi.org/10.1016/j.landurbplan.2018.08.020
Zhang, J. (2004). The diagnosis methods in planning and design(16)-SD method. Chinese Landscape Architecture, 20(10), 54–58.
Zhang, J. (2018). Landscape evaluation of garden plant community in Nantong City based on SD method. Zhejiang Agricultural Sciences, 59(5), 829–832+836. https://doi.org/10.16178/j.issn.0528-9017.20180545
Zhang, Y., Zhang, L., Liu, X., & Xu, T. (2015). Classification of remote sensing images based on Semantic Web. Computer Technology and Development, 25(5), 6.
Zhou, B., Hang, Z., Fernandez, F. X. P., Fidler, S., & Torralba, A. (2017). Scene parsing through ADE20K dataset [Paper presentation]. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA. https://doi.org/10.1109/CVPR.2017.544
Zhou, X. (1995). Aesthetic research in landscape planning. Urban Planning Review, (2), 54–60+65.
Zhou, Y., Varquez, A. C., & Kanda, M. (2019). High-resolution global urban growth projection based on multiple applications of the SLEUTH urban growth model. Scientific Data, 6(1), Article 34. https://doi.org/10.1038/s41597-019-0048-z
Zuo, Z., & Xiaohua, W. (2019). Application of waste materials in rural landscape construction based on SD method. Huazhong Architecture, (12), 68–73. https://doi.org/10.13942/j.cnki.hzjz.2019.12.016
Agnew, J. A. (2011). The SAGE handbook of geographical knowledge. SAGE Publications Ltd. https://doi.org/10.4135/9781446201091
Bell, S. (2012). Landscape: Pattern, perception and process (2nd ed.). E and FN Spoon. https://doi.org/10.4324/9780203120088
Brown, T., Keane, T., & Kaplan, S. (1986). Aesthetics and management: Bridging the gap. Landscape and Urban Planning, 13, 1–10. https://doi.org/10.1016/0169-2046(86)90002-2
Cao, H., Yang, H., Jiang, W., Jianling, Y. E., & Dafang, H. E. (2019). The geological environment suitability assessment of underground space development in Changsha city. China Mining Magazine.
Cao, J., Liang, Y., & Zhang, J. (2004). The scenic investigation and assessment of nature reserves in Beijing. Chinese Landscape Architecture, 7, 67–71.
Cao, J., Wang, J., Wu, X., Ding, C., Wang, W., & Wang, H. (2020). Post-evaluation of urban river open space landscape restoration: A case study of the eastern part of the Inner Qinhuai River in Nanjing. Journal of Nanjing Forestry University, 44(3), 195–201.
Chen, R., Zhao, J., Hao, H., & Wang, K. (2021). A large-scale measurement method of esthetical appreciation laws based on the multimodal machine learning. ZHUANGSHI, 7, 106–111. https://doi.org/10.16272/j.cnki.cn11-1392/j.2021.07.022
Coeterier, J. (1996). Dominant attributes in the perception and evaluation of the Dutch landscape. Landscape and Urban Planning, 34(1), 27–44. https://doi.org/10.1016/0169-2046(95)00204-9
Cong, X., Yin, M., Ding, S., Wang, x., & Zhang, Q. (2021). A study on the perception and evaluation of the popularity of scenic spots of Chinese classical Gardens and the way of dissemination from the perspective of tourists: A case study of the Lingering Garden of Classical Gardens in Suzhou. Chinese Landscape Architecture, 37(08), 56–61.
Dai, L., Zheng, C., Dong, Z., Yao, Y., Wang, R., Zhang, X., Ren, S., Zhang, J., Song, X., & Guan, Q. (2021). Analyzing the correlation between visual space and residents’ psychology in Wuhan, China using street-view images and deep-learning technique. City and Environment Interactions, 11, Article 100069. https://doi.org/10.1016/j.cacint.2021.100069
Daniel, T. C. (2001). Whither scenic beauty? Visual landscape quality assessment in the 21st century. Landscape and Urban Planning, 54(1–4), 267–281. https://doi.org/10.1016/S0169-2046(01)00141-4
de Val, G. d. l. F., Atauri, J. A., & de Lucio, J. V. (2006). Relationship between landscape visual attributes and spatial pattern indices: A test study in Mediterranean-climate landscapes. Landscape and Urban Planning, 77(4), 393–407. https://doi.org/10.1016/j.landurbplan.2005.05.003
Dearnley, C. (2005). A reflection on the use of semi-structured interviews. Nurse Researcher, 13(1). https://doi.org/10.7748/nr2005.07.13.1.19.c5997
Dubey, A., Naik, N., Parikh, D., Raskar, R., & Hidalgo, C. A. (2016, October 11–14). Deep learning the city: Quantifying urban perception at a global scale [Paper presentation]. Computer Vision – ECCV 2016: Proceedings of the 14th European Conference, Amsterdam, The Netherlands. Springer. https://doi.org/10.1007/978-3-319-46448-0_12
Fletcher, R., Baulcomb, C., Hall, C., & Hussain, S. (2014). Revealing marine cultural ecosystem services in the Black Sea. Marine Policy, 50, 151–161. https://doi.org/10.1016/j.marpol.2014.05.001
Gebru, T., Krause, J., Wang, Y., Chen, D., Deng, J., Aiden, E. L., & Fei-Fei, L. (2017). Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States. Proceedings of the National Academy of Sciences, 114, 13108–13113. https://doi.org/10.1073/pnas.1700035114
Godbey, G., Mowen, A., & Ashburn, V. (2010). The benefits of physical activity provided by park and recreation services: The scientific evidence. National Recreation and Park Association Ashburn, VA.
Goodchild, M. F. (2010). Formalizing place in geographic information systems. In Communities, neighborhoods, and health: Expanding the boundaries of place (pp. 21–33). Springer. https://doi.org/10.1007/978-1-4419-7482-2_2
Gozalo, G. R., Morillas, J. M. B., González, D. M., & Moraga, P. A. (2018). Relationships among satisfaction, noise perception, and use of urban green spaces. Science of the Total Environment, 624, 438–450. https://doi.org/10.1016/j.scitotenv.2017.12.148
Guang, G. (2020). Research on evaluation of roof greening landscape based on visual perception. Huaqiao University.
Hadavi, S. (2017). Direct and indirect effects of the physical aspects of the environment on mental well-being. Environment and Behavior, 49(10), 1071–1104. https://doi.org/10.1177/0013916516679876
Hägerhäll, C. M., Ode Sang, Å., Englund, J.-E., Ahlner, F., Rybka, K., Huber, J., & Burenhult, N. (2018). Do humans really prefer semi-open natural landscapes? A cross-cultural reappraisal. Frontiers in Psychology, 9, Article 822. https://doi.org/10.3389/fpsyg.2018.00822
Han, G., & Zhu, Y. (2021). Analysis of application and practice essentials of landscape planning and design in leisure agriculture park–Taking Jiutai Ecotourism Resort in Fujian Province as an example. Journal of Jilin Agricultural Science and Technology University, 30(01), 20–22+32.
Hand, K. L., Freeman, C., Seddon, P. J., Recio, M. R., Stein, A., & Van Heezik, Y. (2017). The importance of urban gardens in supporting children’s biophilia. Proceedings of the National Academy of Sciences, 114(2), 274–279. https://doi.org/10.1073/pnas.1609588114
Helbich, M., Yao, Y., Liu, Y., Zhang, J., Liu, P., & Wang, R. (2019). Using deep learning to examine street view green and blue spaces and their associations with geriatric depression in Beijing, China. Environment International, 126, 107–117. https://doi.org/10.1016/j.envint.2019.02.013
Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A.-r., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T. N., & Kingsbury, B. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine, 29(6), 82–97. https://doi.org/10.1109/MSP.2012.2205597
Irvine, K. N., Warber, S. L., Devine-Wright, P., & Gaston, K. J. (2013). Understanding urban green space as a health resource: A qualitative comparison of visit motivation and derived effects among park users in Sheffield, UK. International Journal of Environmental Research and Public Health, 10(1), 417–442. https://doi.org/10.3390/ijerph10010417
Jiao, M., Gao, F., Hao, P., & Dong, L. (2013). Evaluation of urban banded park plant landscape based on SD method. Journal of Northwest Forestry College, 28(5), 185–190. https://doi.org/10.3969/j.issn.1001-7461.2013.05.37
Joly, D., Brossard, T., Cavailhès, J., Hilal, M., Tourneux, F.-P., Tritz, C., & Wavresky, P. (2009). A quantitative approach to the visual evaluation of landscape. Annals of the Association of American Geographers, 99(2), 292–308. https://doi.org/10.1080/00045600802708473
Kabisch, N., Qureshi, S., & Haase, D. (2015). Human–environment interactions in urban green spaces–A systematic review of contemporary issues and prospects for future research. Environmental Impact Assessment Review, 50, 25–34. https://doi.org/10.1016/j.eiar.2014.08.007
Kaplan, R., & Kaplan, S. (1989). The experience of nature: A psychological perspective. Cambridge University Press.
Kaplan, S. (1987). Aesthetics, affect, and cognition: Environmental preference from an evolutionary perspective. Environment and Behavior, 19(1), 3–32. https://doi.org/10.1177/0013916587191001
Kruse, J., Kang, Y., Liu, Y. N., Zhang, F., & Gao, S. (2021). Places for play: Understanding human perception of playability in cities using street view images and deep learning. Computers, Environment and Urban Systems, 90, Article 101693. https://doi.org/10.1016/j.compenvurbsys.2021.101693
Laaksonen, P., Laaksonen, M., Borisov, P., & Halkoaho, J. (2006). Measuring image of a city: A qualitative approach with case example. Place Branding, 2, 210–219. https://doi.org/10.1057/palgrave.pb.5990058
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Lei, F. (2020). Evaluation on garden plants landscape based on SD method–A Case in Xiangshihu Campus of Guangxi University of Finance and Economics. Journal of Shandong Agricultural University (Natural Science Edition), 51(5), 858–862. https://doi.org/10.3969/j.issn.1000-2324.2020.05.015
Li, C., Song, Y., Kaza, N., & Burghardt, R. (2023). Explaining spatial variations in residential energy usage intensity in Chicago: The role of urban form and geomorphometry. Journal of Planning Education and Research, 43(2), 317–331. https://doi.org/10.1177/0739456X19873382
Li, X., Ratti, C., & Seiferling, I. (2018). Quantifying the shade provision of street trees in urban landscape: A case study in Boston, USA, using Google Street View. Landscape and Urban Planning, 169, 81–91. https://doi.org/10.1016/j.landurbplan.2017.08.011
Liang, X., Luo, C., & Quan, Y. (2020). Research on progress of image semantic segmentation Based on deep learning. Computer Engineering and Applications, 56(2), 18–28.
Lifang, Q., Yichuan, Z., & Wei, C. (2008). Evaluation of urban river landscape design rationality based on AHP. Water Science and Engineering, 1(4), 75–81.
Lin, Y., Li, J., Li, L., Fu, W., & Dong, J. (2022). Landscape quality evaluation of urban canopy forest trail based on image semantic segmentation: A case study of Fudao in Fuzhou. Safety and Environmental Engineering, 29(03), 218–224+243. https://doi.org/10.13578/j.cnki.issn.1671-1556.20211187
Liu, M., Han, L., Xiong, S., Qing, L., Ji, H., & Peng, Y. (2019, August 23–25). Large-scale street space quality evaluation based on deep learning over street view image [Paper presentation]. Proceedings of the 10th International Conference on Image and Graphics, ICIG 2019, Beijing, China. https://doi.org/10.1007/978-3-030-34110-7_58
Liu, Y., & Xu, F. (2011). Scenic assessment in the districts of economically affordable housing. Journal of Northwest Forestry University, 26(6), 199–203.
Lothian, A. (1999). Landscape and the philosophy of aesthetics: Is landscape quality inherent in the landscape or in the eye of the beholder? Landscape and Urban Planning, 44(4), 177–198. https://doi.org/10.1016/S0169-2046(99)00019-5
Luo, J., Zhao, T., Cao, L., & Biljecki, F. (2022). Semantic Riverscapes: Perception and evaluation of linear landscapes from oblique imagery using computer vision. Landscape and Urban Planning, 228, Article 104569. https://doi.org/10.1016/j.landurbplan.2022.104569
Lynch, K. (1960). The image of the environment. The Image of the City, 11, 1–13.
Mitchell, R. (2013). Is physical activity in natural environments better for mental health than physical activity in other environments? Social Science & Medicine, 91, 130–134. https://doi.org/10.1016/j.socscimed.2012.04.012
Naik, N., Philipoom, J., Raskar, R., & Hidalgo, C. (2014). Streetscore -- Predicting the perceived safety of one million streetscapes [Paper presentation]. IEEE Conference on Computer Vision & Pattern Recognition Workshops, Columbus, OH, USA. https://doi.org/10.1109/CVPRW.2014.121
Nasar, J. L. (1990). The evaluative image of the city. Journal of the American Planning Association, 56(1), 41–53. https://doi.org/10.1177/0739456X9901800312
Ortolano, L. (1984). Environmental planning and decision making. United States. https://www.osti.gov/biblio/5734691
Osgood, C. E., Suci, G. J., & Tannenbaum, P. H. (1957). The measurement of meaning. University of Illinois Press.
Peng, X., & Huang, Z. (2017). A novel popular tourist attraction discovering approach based on geo-tagged social media big data. ISPRS International Journal of Geo-Information, 6(7), Article 216. https://doi.org/10.3390/ijgi6070216
Peng, X., Bao, Y., & Huang, Z. (2020). Perceiving Beijing’s “city image” across different groups based on geotagged social media data. IEEE Access, 8, 93868–93881. https://doi.org/10.1109/ACCESS.2020.2995066
Qiu, H., Che, S., Xie, C., & Pan, H. (2021). Public cognition of Shanghai city streetscape and landscape aesthetics based on deep learning. Chinese Landscape Architecture, 37(06), 77–81. https://doi.org/10.19775/j.cla.2021.06.0077
Qureshi, S., Breuste, J. H., & Lindley, S. J. (2010a). Green space functionality along an urban gradient in Karachi, Pakistan: A socio-ecological study. Human Ecology, 38, 283–294. https://doi.org/10.1007/s10745-010-9303-9
Qureshi, S., Kazmi, S. J. H., & Breuste, J. H. (2010b). Ecological disturbances due to high cutback in the green infrastructure of Karachi: Analyses of public perception about associated health problems. Urban Forestry & Urban Greening, 9(3), 187–198. https://doi.org/10.1016/j.ufug.2009.08.003
Rattenbury, T., & Naaman, M. (2009). Methods for extracting place semantics from Flickr tags. ACM Transactions on the Web, 3(1), Article 1. https://doi.org/10.1145/1462148.1462149
Richardson, E. A., Pearce, J., Mitchell, R., & Kingham, S. (2013). Role of physical activity in the relationship between urban green space and health. Public Health, 127(4), 318–324. https://doi.org/10.1016/j.puhe.2013.01.004
Routledge, B. P. (1993). Terrains of resistance. Praeger. https://doi.org/10.1016/0962-6298(96)00029-7
Salesses, P., Schechtner, K., & Hidalgo, C. A. (2013). The collaborative image of the city: Mapping the inequality of urban perception. PloS One, 8(7), Article e68400. https://doi.org/10.1371/journal.pone.0068400
Song, S., Xu, Y., Wu, Z., Deng, X., & Wang, Q. (2019). The relative impact of urbanization and precipitation on long-term water level variations in the Yangtze River Delta. Science of the Total Environment, 648, 460–471. https://doi.org/10.1016/j.scitotenv.2018.07.433
Tang, Z., & Liu, B. (2015). Progress in visual landscape evaluation. Landscape Architecture, 9, 113–120.
Torralba, A., Russell, B. C., & Yuen, J. (2010). LabelMe: Online image annotation and applications. Proceedings of the IEEE, 98(8), 1467–1484. https://doi.org/10.1109/JPROC.2010.2050290
Tuan, Y.-F. (1977). Space and place: The perspective of experience. University of Minnesota Press.
Tzoulas, K., Korpela, K., Venn, S., Yli-Pelkonen, V., Kaźmierczak, A., Niemela, J., & James, P. (2007). Promoting ecosystem and human health in urban areas using Green Infrastructure: A literature review. Landscape and Urban Planning, 81(3), 167–178. https://doi.org/10.1016/j.landurbplan.2007.02.001
Wan, C., Shen, G. Q., & Choi, S. (2020). Effects of physical and psychological factors on users’ attitudes, use patterns, and perceived benefits toward urban parks. Urban Forestry & Urban Greening, 51, Article 126691. https://doi.org/10.1016/j.ufug.2020.126691
Wei, J., Yue, W., Li, M., & Gao, J. (2022). Mapping human perception of urban landscape from street-view images: A deep-learning approach. International Journal of Applied Earth Observation and Geoinformation, 112, Article 102886. https://doi.org/10.1016/j.jag.2022.102886
Wendel, H. E. W., Zarger, R. K., & Mihelcic, J. R. (2012). Accessibility and usability: Green space preferences, perceptions, and barriers in a rapidly urbanizing city in Latin America. Landscape and Urban Planning, 107(3), 272–282. https://doi.org/10.1016/j.landurbplan.2012.06.003
Wu, X., Huang, Z., Peng, X., Chen, Y., & Liu, Y. (2018). Building a spatially-embedded network of tourism hotspots from geotagged social media data. IEEE Access, 6, 21945–21955. https://doi.org/10.1109/ACCESS.2018.2828032
Xi, Z., Li, C., Zhou, L., Yang, H., & Burghardt, R. (2023). Built environment influences on urban climate resilience: Evidence from extreme heat events in Macau. Science of the Total Environment, 859, Article 160270. https://doi.org/10.1016/j.scitotenv.2022.160270
Xu, Y., Zhang, Z., Yu, D., Yuan, D., & Li, Q. (2022). Semantic segmentation algorithm of dyke scene image based on attention guidance. Information Technology, (05), 88–93+100. https://doi.org/10.13274/j.cnki.hdzj.2022.05.015
Yang, H.-j., Zhu, T.-c., & Maruyama, J. (2004). Research on quantitative evaluation of visual effect of grassland landscapes–with grassland in construction in Sihori, Japan as an example. Acta Prataculturae Sinica, 13(4), 106–111.
Yao, Y., Liang, Z., Yuan, Z., Liu, P., Bie, Y., Zhang, J., Wang, R., Wang, J., & Guan, Q. (2019). A human-machine adversarial scoring framework for urban perception assessment using street-view images. International Journal of Geographical Information Science, 33(12), 2363–2384. https://doi.org/10.1080/13658816.2019.1643024
Yu, K. (1987). A preliminary study on China’s natural wind resources management system. Chinese Garden, (03), 33–37. https://xueshu.baidu.com/usercenter/paper/show?paperid=7b9c09e9f982ddd08a7f62f9477a393c&site=xueshu_se&hitarticle=1
Yuen, H. K., & Jenkins, G. R. (2020). Factors associated with changes in subjective well-being immediately after urban park visit. International Journal of Environmental Health Research, 30(2), 134–145. https://doi.org/10.1080/09603123.2019.1577368
Zhang, F., Zhang, D., Liu, Y., & Lin, H. (2018a). Representing place locales using scene elements. Computers, Environment and Urban Systems, 71, 153–164. https://doi.org/10.1016/j.compenvurbsys.2018.05.005
Zhang, F., Zhou, B., Liu, L., Liu, Y., Fung, H. H., Lin, H., & Ratti, C. (2018b). Measuring human perceptions of a large-scale urban region using machine learning. Landscape and Urban Planning, 180, 148–160. https://doi.org/10.1016/j.landurbplan.2018.08.020
Zhang, J. (2004). The diagnosis methods in planning and design(16)-SD method. Chinese Landscape Architecture, 20(10), 54–58.
Zhang, J. (2018). Landscape evaluation of garden plant community in Nantong City based on SD method. Zhejiang Agricultural Sciences, 59(5), 829–832+836. https://doi.org/10.16178/j.issn.0528-9017.20180545
Zhang, Y., Zhang, L., Liu, X., & Xu, T. (2015). Classification of remote sensing images based on Semantic Web. Computer Technology and Development, 25(5), 6.
Zhou, B., Hang, Z., Fernandez, F. X. P., Fidler, S., & Torralba, A. (2017). Scene parsing through ADE20K dataset [Paper presentation]. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA. https://doi.org/10.1109/CVPR.2017.544
Zhou, X. (1995). Aesthetic research in landscape planning. Urban Planning Review, (2), 54–60+65.
Zhou, Y., Varquez, A. C., & Kanda, M. (2019). High-resolution global urban growth projection based on multiple applications of the SLEUTH urban growth model. Scientific Data, 6(1), Article 34. https://doi.org/10.1038/s41597-019-0048-z
Zuo, Z., & Xiaohua, W. (2019). Application of waste materials in rural landscape construction based on SD method. Huazhong Architecture, (12), 68–73. https://doi.org/10.13942/j.cnki.hzjz.2019.12.016