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The Restorative Effects of Unique Green Space Design: Comparing the Restorative Quality of Classical Chinese Gardens and Modern Urban Parks
The purpose of this paper is to explore the restorative effects of two unique Urban Green Spaces (UGSs): modern urban parks (MUPs) and classical Chinese gardens (CCGs). Compared to MUPs, little research has been conducted on the potential restorative effects of CCGs. To fill this gap, we collected video clips of various scenes in Suzhou, Jiangsu Province, ‘the city of gardens’ in China, and produced video images of CCGs and MUPs representing UGSs. Public ratings of these videos were collected using the Short-Version Revision Repair Scale (SRRS) to analyze the perceived restorative effects and drivers of CCGs and MUPs in combination with landscape elements, which were deeply parsed using computer vision techniques (semantic segmentation techniques) and expert scoring methods. The aim is to enhance the therapeutic effects of CCGs and MUPs. The results indicate that the differences between the two landscape types in terms of their restorative effects are not significant. The videos representing CCGs had a greater standard deviation of restorative effects (a lower level of consensus) compared to the videos depicting MUPs. Deep learning techniques for semantic segmentation combined with expert scoring methods can effectively help us to understand the drivers influencing restorative effects, and we combined our findings to conclude that improved waterscape design is an essential driver for enhancing the perceived restorative effects of CCGs and that decreasing specific artificial modern structures, enhancing vegetation cover, and increasing public exposure to nature are critical to strengthening the restorative effects of both. Hopefully, these findings will improve visitors’ recovery in UGS environments and guide landscape architects to more effectively design healing UGSs.
The Restorative Effects of Unique Green Space Design: Comparing the Restorative Quality of Classical Chinese Gardens and Modern Urban Parks
The purpose of this paper is to explore the restorative effects of two unique Urban Green Spaces (UGSs): modern urban parks (MUPs) and classical Chinese gardens (CCGs). Compared to MUPs, little research has been conducted on the potential restorative effects of CCGs. To fill this gap, we collected video clips of various scenes in Suzhou, Jiangsu Province, ‘the city of gardens’ in China, and produced video images of CCGs and MUPs representing UGSs. Public ratings of these videos were collected using the Short-Version Revision Repair Scale (SRRS) to analyze the perceived restorative effects and drivers of CCGs and MUPs in combination with landscape elements, which were deeply parsed using computer vision techniques (semantic segmentation techniques) and expert scoring methods. The aim is to enhance the therapeutic effects of CCGs and MUPs. The results indicate that the differences between the two landscape types in terms of their restorative effects are not significant. The videos representing CCGs had a greater standard deviation of restorative effects (a lower level of consensus) compared to the videos depicting MUPs. Deep learning techniques for semantic segmentation combined with expert scoring methods can effectively help us to understand the drivers influencing restorative effects, and we combined our findings to conclude that improved waterscape design is an essential driver for enhancing the perceived restorative effects of CCGs and that decreasing specific artificial modern structures, enhancing vegetation cover, and increasing public exposure to nature are critical to strengthening the restorative effects of both. Hopefully, these findings will improve visitors’ recovery in UGS environments and guide landscape architects to more effectively design healing UGSs.
The Restorative Effects of Unique Green Space Design: Comparing the Restorative Quality of Classical Chinese Gardens and Modern Urban Parks
Zhenyu Zhang (author) / Mu Jiang (author) / Jingwei Zhao (author)
2024
Article (Journal)
Electronic Resource
Unknown
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