Quantitative analysis and evaluation of winter and summer landscape colors in the Yangzhou ancient Canal utilizing deep learning
Abstract Color is an important index for human visual evaluation of landscape, and it is also a key factor affecting people’s recognition and experience of heritage landscape. In this study, five important sites of the Yangzhou Grand Canal were selected for the color quantification analysis by using...
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2025-03-01
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| author | Yanyan Wang Jiangling Qian Jiajie Cao Rong Fan Xunyu Han |
| author_facet | Yanyan Wang Jiangling Qian Jiajie Cao Rong Fan Xunyu Han |
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| description | Abstract Color is an important index for human visual evaluation of landscape, and it is also a key factor affecting people’s recognition and experience of heritage landscape. In this study, five important sites of the Yangzhou Grand Canal were selected for the color quantification analysis by using the Deep Learning(DL) scene parsing algorithm. The color characteristics of the winter and summer landscape of the five sites were evaluated as well as the Scenic Beauty Estimation (SBE) value. Furthermore, the correlation analysis between the color characteristics and the SBE value was established in order to study the relationship between color characteristics and the landscape beauty. The main results are as follows: ①.The dominant color of the five sites is blue and green, the building color is mainly orange and yellow in both winter and summer. The dominant plant color in five sites is green in summer, whereas in winter, changes to yellow(Site5:YZJGD) or cyan(Site1:DGGD, Site3:GZGD); ②.The overall color saturation is low in winter with the percentages of Very Low Saturation in almost each site(except site5:YZJGD)reach 80–98%. Summer has Medium Saturation colors, the percentage of Mid Saturation of sky in Site 2(GMS) in summer is 44.87%. ③. The landscapes have low brightness in winter and higher brightness in summer in all sites, sky is the only category whose High Brightness value exceeds 50% in both seasons.And in winter, landscapes are most prevalent in Low Brightness and Medium Brightness. In summer, the percentages of Medium Brightness and High Brightness increase.④.The color diversity of the sites in winter varies significantly, whereas the color diversity of the sites in summer varies slightly.The highest color diversity of plants is found in DGGD(Diversity > 1.5). ⑤.In winter, the highest SBE value is found in Site2:GMS(0.5956), and the lowest SBE value is found in Site5:YZJGD(− 0.8216),which is a large gap(1.4172).The highest average SBE value is in Site2:GMS(0.5062), followed by Site3:GZGD (0.2091), which both have average values greater than zero. ⑥.Correlation analysis revealed that there is no significant correlation between the saturation and SBE values(p > 0.05).However, the Pearson correlation coefficients which are − 0.625(winter) and 0.689(summer) indicate strong correlation.Meanwhile, there is no significant correlation between the color diversity and SBE values(p > 0.05). However, the Pearson correlation coefficients are 0.807(winter) and − 0.747(summer), indicating strong correlation.This study provides an in-depth examination of the Canal landscape color, it is hoped to promote the systematic and scientific study of landscape colors and provide a theoretical basis for the scientific design of heritage landscape color. |
| format | Article |
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| spelling | doaj-art-e255b2b70b6e402da85b2affc8591c8c2025-08-20T03:05:52ZengNature PortfolioScientific Reports2045-23222025-03-0115112310.1038/s41598-025-91483-1Quantitative analysis and evaluation of winter and summer landscape colors in the Yangzhou ancient Canal utilizing deep learningYanyan Wang0Jiangling Qian1Jiajie Cao2Rong Fan3Xunyu Han4College of Landscape Architecture, Nanjing Forestry UniversityCollege of Landscape Architecture, Nanjing Forestry UniversityCollege of Landscape Architecture, Nanjing Forestry UniversityCollege of Landscape Architecture, Nanjing Forestry UniversityCollege of Landscape Architecture, Nanjing Forestry UniversityAbstract Color is an important index for human visual evaluation of landscape, and it is also a key factor affecting people’s recognition and experience of heritage landscape. In this study, five important sites of the Yangzhou Grand Canal were selected for the color quantification analysis by using the Deep Learning(DL) scene parsing algorithm. The color characteristics of the winter and summer landscape of the five sites were evaluated as well as the Scenic Beauty Estimation (SBE) value. Furthermore, the correlation analysis between the color characteristics and the SBE value was established in order to study the relationship between color characteristics and the landscape beauty. The main results are as follows: ①.The dominant color of the five sites is blue and green, the building color is mainly orange and yellow in both winter and summer. The dominant plant color in five sites is green in summer, whereas in winter, changes to yellow(Site5:YZJGD) or cyan(Site1:DGGD, Site3:GZGD); ②.The overall color saturation is low in winter with the percentages of Very Low Saturation in almost each site(except site5:YZJGD)reach 80–98%. Summer has Medium Saturation colors, the percentage of Mid Saturation of sky in Site 2(GMS) in summer is 44.87%. ③. The landscapes have low brightness in winter and higher brightness in summer in all sites, sky is the only category whose High Brightness value exceeds 50% in both seasons.And in winter, landscapes are most prevalent in Low Brightness and Medium Brightness. In summer, the percentages of Medium Brightness and High Brightness increase.④.The color diversity of the sites in winter varies significantly, whereas the color diversity of the sites in summer varies slightly.The highest color diversity of plants is found in DGGD(Diversity > 1.5). ⑤.In winter, the highest SBE value is found in Site2:GMS(0.5956), and the lowest SBE value is found in Site5:YZJGD(− 0.8216),which is a large gap(1.4172).The highest average SBE value is in Site2:GMS(0.5062), followed by Site3:GZGD (0.2091), which both have average values greater than zero. ⑥.Correlation analysis revealed that there is no significant correlation between the saturation and SBE values(p > 0.05).However, the Pearson correlation coefficients which are − 0.625(winter) and 0.689(summer) indicate strong correlation.Meanwhile, there is no significant correlation between the color diversity and SBE values(p > 0.05). However, the Pearson correlation coefficients are 0.807(winter) and − 0.747(summer), indicating strong correlation.This study provides an in-depth examination of the Canal landscape color, it is hoped to promote the systematic and scientific study of landscape colors and provide a theoretical basis for the scientific design of heritage landscape color.https://doi.org/10.1038/s41598-025-91483-1Landscape colorQuantitative color analysisDeep learningLandscape beauty EstimationCorrelation analysis |
| spellingShingle | Yanyan Wang Jiangling Qian Jiajie Cao Rong Fan Xunyu Han Quantitative analysis and evaluation of winter and summer landscape colors in the Yangzhou ancient Canal utilizing deep learning Scientific Reports Landscape color Quantitative color analysis Deep learning Landscape beauty Estimation Correlation analysis |
| title | Quantitative analysis and evaluation of winter and summer landscape colors in the Yangzhou ancient Canal utilizing deep learning |
| title_full | Quantitative analysis and evaluation of winter and summer landscape colors in the Yangzhou ancient Canal utilizing deep learning |
| title_fullStr | Quantitative analysis and evaluation of winter and summer landscape colors in the Yangzhou ancient Canal utilizing deep learning |
| title_full_unstemmed | Quantitative analysis and evaluation of winter and summer landscape colors in the Yangzhou ancient Canal utilizing deep learning |
| title_short | Quantitative analysis and evaluation of winter and summer landscape colors in the Yangzhou ancient Canal utilizing deep learning |
| title_sort | quantitative analysis and evaluation of winter and summer landscape colors in the yangzhou ancient canal utilizing deep learning |
| topic | Landscape color Quantitative color analysis Deep learning Landscape beauty Estimation Correlation analysis |
| url | https://doi.org/10.1038/s41598-025-91483-1 |
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