Research on the Perception Evaluation of Urban Green Spaces Using Panoramic Images and Deep Learning: A Case Study of Zhujiang Park in Guangzhou

Highlights

	● Explores a convenient image collection and processing workflow using panoramic cameras for urban green spaces
	● Develops a deep-learning-based evaluation method for park landscape visual quality, enabling unbiased analysis
	● Applies quantitative computation and statistical approaches to rapidly identifying areas needing optimization measures by integrating subjective and objective evaluation metrics

Abstract

Visual quality assessment of urban green spaces is a major topic in landscape architecture research, yet traditional methods face limitations in practice. The rapid development of artificial intelligence and street-view big data offers opportunities for advancing green space perception studies. However, the lack of full street view image coverage of green spaces in China poses challenges for related research. Focusing on public landscape perception evaluation, this research took Zhujiang Park in Guangzhou, China as a case study. The research team utilized a convenient image collection method by panoramic camera and an effective processing workflow, and then employed the Segformer-B5 semantic segmentation model and the ViT-base-p16 image classification model to calculate four objective evaluation metrics (green view index, sky view factor, road visibility index, and artificial structure visibility index) and four subjective evaluation metrics (attractiveness, richness, naturalness, and depression) for visual quality assessment. Based on the spatial distribution results of these metrics, comprehensive analyses were conducted and low-score areas were identified. Research results indicate that vegetation and water features significantly enhance park attractiveness and positive perceptions, while excessive sky and artificial structures produce negative effects; oppressive artificial landscapes and constrained architectural views also lower overall landscape quality. The image collection and visual perception evaluation methods proposed in this study provide a scientific basis for the renovation and management of urban green spaces.

Graphical abstract

Keywords

Landscape Perception Evaluation / Visual Landscape Assessment / Panoramic Camera / Artificial Intelligence / Urban Green Space / Semantic Segmentation / Image Classification

References

[1] Wolch, J. R. , Byrne, J. , & Newell, J. P. (2014) Urban green space, public health, and environmental justice: The challenge of making cities 'just green enough'. Landscape and Urban Planning, ( 125), 234– 244.

[2] Daniel, T. C. (2001) Whither scenic beauty? Visual landscape quality assessment in the 21st century. Landscape and Urban Planning, 54 ( 1–4), 267– 281.

[3] Gobster, P. H. , Ribe, R. G. , & Palmer, J. F. (2019) Themes and trends in visual assessment research: Introduction to the Landscape and Urban Planning special collection on the visual assessment of landscapes. Landscape and Urban Planning, ( 191), 103635.

[4] Daniel, T. C. (1976). Measuring Landscape Esthetics: The Scenic Beauty Estimation Method. Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station.

[5] Cai, K. , Huang, W. , & Lin, G. (2022) Bridging landscape preference and landscape design: A study on the preference and optimal combination of landscape elements based on conjoint analysis. Urban Forestry & Urban Greening, ( 73), 127615.

[6] Zhao, X. , Lu, Y. , & Lin, G. (2024) An integrated deep learning approach for assessing the visual qualities of built environments utilizing street view images. Engineering Applications of Artificial Intelligence, ( 130), 107805.

[7] He, N. , & Li, G. (2021) Urban neighbourhood environment assessment based on street view image processing: A review of research trends. Environmental Challenges, ( 4), 100090.

[8] Sanchez, T. W. , Shumway, H. , Gordner, T. , & Lim, T. (2022) The prospects of artificial intelligence in urban planning. International Journal of Urban Sciences, 27 ( 2), 179– 194.

[9] Cheng, Y. , & Fan, B. (2023) Digital landscape process. Chinese Landscape Architecture, 39 ( 6), 6– 12.

[10] Biljecki, F. , & Ito, K. (2021) Street view imagery in urban analytics and GIS: A review. Landscape and Urban Planning, ( 215), 104217.

[11] 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), 104569.

[12] Li, Y. , & Long, Y. (2024) Inferring storefront vacancy using mobile sensing images and computer vision approaches. Computers, Environment and Urban Systems, ( 108), 102071.

[13] Xie, E. , Wang, W. , Yu, Z. , Anandkumar, A. , Alvarez, J. M. , & Luo, P. (2021) SegFormer: Simple and efficient design for semantic segmentation with transformers. Advances in Neural Information Processing Systems, ( 34), 12077– 12090.

[14] Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., & Torralba, A. (2017). Scene parsing through ADE20K dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 633–641). Computer Vision Foundation.

[15] Qiu, W. , Li, W. , Liu, X. , Zhang, Z. , Li, X. , & Huang, X. (2023) Subjective and objective measures of streetscape perceptions: Relationships with property value in Shanghai. Cities, ( 132), 104037.

[16] Song, Q., Li, W., Li, M., & Qiu, W. (2022). Social inequalities in neighborhood-level streetscape perceptions in Shanghai: The coherence and divergence between the objective and subjective measurements. Social Science Research Network.

[17] Xia, Y. , Yabuki, N. , & Fukuda, T. (2021) Sky view factor estimation from street view images based on semantic segmentation. Urban Climate, ( 40), 100999.

[18] Lange, E. , & Legwaila, I. (2012) Visual landscape research—Overview and outlook. Chinese Landscape Architecture, 28 ( 3), 5– 14.

[19] Dubey, A., Naik, N., Parikh, D., Raskar, R., & Hidalgo, C. A. (2016). Deep learning the city: Quantifying urban perception at a global scale. Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I (pp. 196–212). Springer.

[20] Sun, D. , Li, Q. , Gao, W. , Huang, G. , Tang, N. , Lyu, M. , & Yu, Y. (2021) On the relation between visual quality and landscape characteristics: A case study application to the waterfront linear parks in Shenyang, China. Environmental Research Communications, 3 ( 11), 115013.

[21] Zhang, G. , Yang, J. , & Jin, J. (2021) Assessing relations among landscape preference, informational variables, and visual attributes. Journal of Environmental Engineering and Landscape Management, 29 ( 3), 294– 304.

[22] Wartmann, F. M. , Stride, C. , Kienast, F. , & Hunziker, M. (2021) Relating landscape ecological metrics with public survey data on perceived landscape quality and place attachment. Landscape Ecology, ( 36), 2367– 2393.

[23] "Depressing. " Oxford English Dictionary. Oxford University Press.

[24] Gong, Y. , Palmer, S. , Gallacher, J. , Marsden, T. , & Fone, D. (2016) A systematic review of the relationship between objective measurements of the urban environment and psychological distress. Environment International, ( 96), 48– 57.

[25] Zhang, F. , Zhou, B. , Liu, L. , Fung, H. H. , Lin, H. , & Ratti, C. (2018) Measuring human perceptions of a large-scale urban region using machine learning. Landscape and Urban Planning, ( 180), 148– 160.

[26] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An image is worth 16x16 words: Transformers for image recognition at scale. International Conference on Learning Representations.

[27] Talal, M. L. , Santelmann, M. V. , & Tilt, J. H. (2021) Urban park visitor preferences for vegetation—An on-site qualitative research study. Plants, People, Planet, 3 ( 4), 375– 388.

[28] Council of Europe. (2000). Explanatory Report to the European Landscape Convention.

Acknowledgements

·Project of "A study on the Inclusive Design in the Recreational Place of Urban Green Space in Response to Passive and Active Exclusion, " National Natural Science Foundation of China (No. 52378054) ·Project of "Research on Green Space Supply Evaluation Methods Based on Public Perception, " Fundamental Research Funds for the Central Universities (No. CGPY202410) ·Project of "Research and Application of Deep Learning-Driven Park Perception Evaluation Methods, " South China University of Technology Step Climbing Program (No. j2tw202402095)

RIGHTS & PERMISSIONS

© Higher Education Press 2024