Using Deep Learning Approaches to Quantify Landscape Preference of the Chinese Grand Canal: An Empirical Case Study of the Yangzhou Ancient Canal
Abstract
:1. Introduction
2. Literature Background
3. Materials and Methods
3.1. Study Area and Data Sources
3.1.1. Study Area
3.1.2. Data Acquisition and Pre-Processing
3.2. Research Design
3.3. Data Processing
3.3.1. Image Content Analysis Based on Deep Learning Models
3.3.2. Social Network Analysis of Labels
3.3.3. Point Density Analysis of Geotagged Photos
4. Results
4.1. Yangzhou Ancient Canal Landscape Elements
4.1.1. Baidu Universal Object and Scene Recognition API Results and Label Extraction
4.1.2. Analysis of Object Detection Model Results
4.2. Yangzhou Ancient Canal Landscape Elements
4.3. Patterns of Combining Elements of Different Landscape Types
4.3.1. Network Characteristics
4.3.2. Combination of Elements of Landscape Types
4.4. Spatial Structure of Landscape Preferences in the Yangzhou Ancient Canal
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Zhang, F.; Qiu, B. The Attributes of the Chinese Grand Canal’s Tangible Cultural Heritage Recognition and Practical Reflection. Acad. Bimest. 2021, 5, 146–151. [Google Scholar] [CrossRef]
- Qiu, B.; Zhang, F. Collective Memory and the Understanding and Description of the Chinese Grand Canal’s Original Landscape. Southeast Cult. 2021, 280, 23–28. [Google Scholar]
- Lu, L.; Wang, S. The Study on Cultural Heritage of the Chinese Grand Canal: The Achievements, the Lack and the Advisements. Agric. Hist. China 2019, 38, 137–145. [Google Scholar]
- Wang, X. The Preservation of the Chinese Grand Canal (Hangzhou Section) in the Post-UNESCO World-Heritage-Bid Period: ‘Integrity’ as a Focus. Southeast Cult. 2016, 254, 20–26. [Google Scholar]
- Kaplan, R. The Analysis of Perception via Preference: A Strategy for Studying How the Environment Is Experienced. Landsc. Plan. 1985, 12, 161–176. [Google Scholar] [CrossRef]
- Kaplan, R.; Kaplan, S.; Brown, T. Environmental Preference: A Comparison of Four Domains of Predictors. Environ. Behav. 1989, 21, 509–530. [Google Scholar] [CrossRef]
- Zube, E.H.; Sell, J.L.; Taylor, J.G. Landscape Perception: Research, Application and Theory. Landsc. Plan. 1982, 9, 1–33. [Google Scholar] [CrossRef]
- Wu, J.; Xu, Y.; Ren, J.; Zhang, W.; Li, M.; Pu, H. Progress and Geographical Response on Landscape Preference. Sci. Geogr. Sin. 2021, 41, 656–663. [Google Scholar] [CrossRef]
- Zhang, G.; Fu, B.; Zhao, X.; Tao, S. An Exploration on Linear Cultural Heritage Protection and Inheritance Path of the Grand Canal. City Plan. Rev. 2022, 46, 27–35. [Google Scholar]
- Tieskens, K.F.; Van Zanten, B.T.; Schulp, C.J.E.; Verburg, P.H. Aesthetic Appreciation of the Cultural Landscape through social media: An Analysis of Revealed Preference in the Dutch River Landscape. Landsc. Urban Plan. 2018, 177, 128–137. [Google Scholar] [CrossRef]
- Chesnokova, O.; Purves, R.S. From Image Descriptions to Perceived Sounds and Sources in Landscape: Analyzing Aural Experience through Text. Appl. Geogr. 2018, 93, 103–111. [Google Scholar] [CrossRef]
- Wen, X.; Xu, H.; Wu, D. Research on Bilingual Ontology Construction of Linear Cultural Heritage: A Case Study of Jiaxing Canal Culture. Libr. Inf. Serv. 2023, 67, 107–120. [Google Scholar] [CrossRef]
- Xu, F.; Wang, Z.; Zhang, H. Application of Short Video in Digital Communication of Grand Canal’s Linear Cultural Heritage. Arch. Constr. 2022, 9, 51–54. [Google Scholar]
- Wu, J.; Li, H.; Deng, T.; Luo, S.; Hu, J.; Zhang, Z.; Niu, C.; Wang, K.; Jiang, Y.; Su, S. Comprehensive Land Consolidation and Linear Cultural Heritage Protection, Utilization, Cohesion and Integration: A Case Study of the Gutian Section of the Meiguan Historical Trail, South China Historical Trail, Guangdong Province. Trop. Geogr. 2024, 44, 379–392. [Google Scholar]
- Sun, B.; Guo, F.; Li, R.; Chen, X. Linear cultural heritage landscape visual perception location model and demonstration. Prog. Geogr. 2024, 43, 80–92. [Google Scholar] [CrossRef]
- Xu, Y.; Yang, Y. Hangzhou’s exploration on the enhancement of the Chinese Grand Canal landscape in the post-heritage era: A case study of the Hangzhou Section of the Beijing-Hangzhou Canal. Beijing Plan. Rev. 2020, 194, 36–40. [Google Scholar]
- Wang, J.; Han, B.; Liu, H.; Xi, R. Research on the landscape modeling of Beijing Chinese Grand Canal cultural belt. Chin. Landsc. Archit. 2021, 37 (Suppl. S1), 172–177. [Google Scholar] [CrossRef]
- Wang, H.; Wang, C.; Zhang, C. Study on seasonal appearance of plant landscape in Tongzhou Canal Forest Park. Chin. Landsc. Archit. 2020, 36, 128–133. [Google Scholar] [CrossRef]
- Wang, Y.; Tong, H. Analysis of the characteristics of the Jining ancient city scenic system under the influence of the Lu Canal. Chin. Landsc. Archit. 2021, 37, 62–67. [Google Scholar] [CrossRef]
- Liu, L. Research on the Waterfront Features of Jiangnan Area of the Chinese Grand Canal in Qing Dynasty. Master’s Thesis, Nanjing Forestry University, Nanjing, China, 2022. [Google Scholar]
- Sun, J. Analysis of urbanization paths and urban form in the Northern Canal regions of the Ming and Qing Dynasties: A case study of Jining. J. Chin. Hist. Stud. 2016, 151, 145–174. [Google Scholar]
- Zhang, C.; Wang, C.; Sun, R.; Jin, J.; Tang, S. Landscape ecology-scale comprehensive assessment of the characteristics of stream riparian-view in urban areas: A case study in Jinjiang City, Fujian Province. Acta Ecol. Sin. 2018, 38, 8526–8535. [Google Scholar]
- Lin, Z.; Shen, D. Scene evaluation and protection in Nanning traditional villages. Planners 2016, 32 (Suppl. S2), 105–109. [Google Scholar]
- Xu, H.; Cao, L.; Li, H. Visual Impact Assessment of Heritage Landscape in Surrounding Environment along the Grand Canal Huai’an Section. Areal Res. Dev. 2021, 40, 171–176. [Google Scholar]
- Li, Z.; Che, T.; Tang, X.; Li, C.; Zhou, C.; Luo, Y.; Zhong, L.; Yu, W.; Xu, Y. Evolution characteristics of landscape pattern and ecosystem services in areas along the Yangzhou section of the Grand Canal. J. Yangzhou Univ. (Agric. Life Sci. Ed.) 2022, 43, 132–137+144. [Google Scholar] [CrossRef]
- Wei, Y.; Xu, H. The Heritage Elements and Its Evaluation of the Grand Canal—Taking Yangzhou as Example. Archit. Cult. 2010, 8, 94–97. [Google Scholar]
- Zhang, X.; Jiang, X.; Zhang, S.; Wang, X.; Lin, Q. Public Perception of the Grand Canal Cultural Heritage Development: Semantic Analysis Based on Big Data Media. Chin. Landsc. Archit. 2022, 38, 52–57. [Google Scholar] [CrossRef]
- Tang, H.; Hong, Q.; Li, J.; Zhao, X.; Wu, F. Research on Post Occupancy Evaluation of Grand Canal Historic and Cultural District Based on Web Review Text Analysis: A Case Study of Hangzhou. Mod. Urban Res. 2021, 7, 28–37. [Google Scholar]
- Ding, Y. Tourists’ Spatial-Temporal Behavior Characteristics and Landscape Preference Based on Geotagged Photos. Master’s Thesis, Central South University of Forestry and Technology, Changsha, China, 2022. [Google Scholar]
- Sonter, L.J.; Watson, K.B.; Wood, S.A.; Ricketts, T.H. Spatial and Temporal Dynamics and Value of Nature-Based Recreation, Estimated via Social Media. PLoS ONE 2016, 11, e0162372. [Google Scholar] [CrossRef] [PubMed]
- Bubalo, M.; van Zanten, B.T.; Verburg, P.H. Crowdsourcing Geo-Information on Landscape Perceptions and Preferences: A Review. Landsc. Urban Plan. 2019, 184, 101–111. [Google Scholar] [CrossRef]
- Fan, M.; Zhang, H.; Chen, Y. Spatiotemporal analysis of visual tourism images in Inner Mongolia from the perspective of tourists. J. Arid Land Resour. Environ. 2020, 34, 194–200. [Google Scholar]
- Cao, Y.; Long, Y.; Yang, P. City image study based on online pictures: 24 Cities Case. Planners 2017, 33, 61–67. [Google Scholar]
- Cao, Y.; Yang, P.; Zhuang, K. Applying Machine Learning in Measuring the Perception of Historical Space. Planners 2021, 37, 67–73. [Google Scholar]
- Lee, S.-H. Visual Preference of Urban Landscape. City Plan. 1999, 26, 19–40. [Google Scholar]
- Pan, Y.; Yang, M.; Li, J. Review of study on foreign landscape preference literature. J. Shandong For. Sci. Technol. 2020, 50, 64–71. [Google Scholar]
- Dunkel, A. Visualizing the Perceived Environment Using Crowdsourced Photo Geodata. Landsc. Urban Plan. 2015, 142, 173–186. [Google Scholar] [CrossRef]
- Song, Q. A Study on Tourists’ Rural Tourism Landscape Preference Based on Internet Data. Master’s Thesis, Northwest A&F University, Xianyang, China, 2021. [Google Scholar]
- Li, X.; Zhao, Z.; Zhang, Y.; Zhong, M. Backpackers’ landscape preference and spatial structure based on photo analysis: Take the Qinling Taibai Mountain’s backpackers as an example. Areal Res. Dev. 2020, 39, 114–120. [Google Scholar]
- Deng, N.; Liu, Y.; Niu, Y.; Ji, W. Different perceptions of Beijing’s destination images from tourists: An analysis of Flickr photos based on deep learning method. Resour. Sci. 2019, 41, 416–429. [Google Scholar] [CrossRef]
- Chen, W.; Tong, J.; He, R.; Lin, Y.; Chen, P.; Chen, Z.; Liu, X. An Easy Method for Identifying 315 Categories of Commonly-Used Chinese Herbal Medicines Based on Automated Image Recognition Using AutoML Platforms. Inform. Med. Unlocked 2021, 25, 100607. [Google Scholar] [CrossRef]
- Wu, Z.; Wu, S.; Yang, H.; Mao, Z.; Shen, W. Enhancing Water Depth Estimation from Satellite Images Using Online Machine Learning: A Case Study Using Baidu Easy-DL with Acoustic Bathymetry and Sentinel-2 Data. Remote Sens. 2023, 15, 4955. [Google Scholar] [CrossRef]
- Ma, X.; Qiu, H. A study on landscape image and public perception and preferences of lake parks based on Internet photo data and Auto ML model. Chin. Landsc. Archit. 2022, 38, 86–91. [Google Scholar] [CrossRef]
- Song, X.P.; Richards, D.R.; Tan, P.Y. Using Social Media User Attributes to Understand Human–Environment Interactions at Urban Parks. Sci. Rep. 2020, 10, 808. [Google Scholar] [CrossRef]
- Li, L. Identification research of tangible and intangible attribute value of urban heritage based on deep learning: A case study of Suzhou River. Urban Dev. Stud. 2021, 28, 104–110. [Google Scholar]
- Li, Y.; Li, M. The Chinese Grand Canal and Yangzhou cultural genes. Agric. Hist. China 2021, 40, 122–130. [Google Scholar]
- Wang, Y.; Wang, H.; Tang, X. A research review of composition and value of Chinese Chinese Grand Canal heritage—Based on database of CNKI China knowledge network. J. Nanjing For. Univ. (Humanit. Soc. Sci. Ed.) 2019, 19, 70–83. [Google Scholar] [CrossRef]
- Lin, H. Construction of Canal Heritage Corridor of Yangzhou Section of Beijing-Hangzhou Grand Canal and Design of Zhuyu Bay. Master’s Thesis, Beijing Forestry University, Beijing, China, 2021. [Google Scholar]
- Wang, T.; Wu, B. Research on characteristics of place image in Beijing city songs based on keywords co-occurrence analysis and social network analysis. Hum. Geogr. 2020, 35, 57–65. [Google Scholar] [CrossRef]
- Freeman, L. The Development of Social Network Analysis. Study Sociol. Sci. 2004, 687, 159–167. [Google Scholar]
- Dempwolf, C.S.; Lyles, L.W. The Uses of Social Network Analysis in Planning: A Review of the Literature. J. Plan. Lit. 2012, 27, 3–21. [Google Scholar] [CrossRef]
- Ji, Y.; Tian, W.; Dong, Y. Thematic Study of Think Tank Hot Spots in China Based on Keywords Co-Occurrence Analysis and Social Network Analysis. Inf. Sci. 2015, 33, 108–111. [Google Scholar] [CrossRef]
- Wasserman, S.; Faust, K. Social Network Analysis: Methods and Applications; Cambridge University Press: Cambridge, UK, 1994; ISBN 978-0-521-38707-1. [Google Scholar]
- Tabassum, S.; Pereira, F.S.F.; Fernandes, S.; Gama, J. Social Network Analysis: An Overview. WIREs Data Min. Knowl. Discov. 2018, 8, e1256. [Google Scholar] [CrossRef]
- Haythornthwaite, C. Social Network Analysis: An Approach and Technique for the Study of Information Exchange. Libr. Inf. Sci. Res. 1996, 18, 323–342. [Google Scholar] [CrossRef]
- Rao, J.; Ma, R. Improved Kernel Density Estimator Based Spatial Point Density Algorithm. Comput. Eng. Appl. 2021, 57, 260–265. [Google Scholar]
- Zhang, Y.; Liu, K.; Chen, W. Study of Site Selection for Central Village Construction Based on AHP and GIS: A Case of Xicun Town of Gongyi City. Areal Res. Dev. 2013, 32, 149–153. [Google Scholar]
- Chen, X.; Wang, Y.; Xi, Z. Study on the Ecological Path of the Construction of Beijing Grand Canal National Cultural Park. Urban Dev. Stud. 2022, 29, 38–43. [Google Scholar]
- Tian, L. Exploration of the methods of waterfront landscaping in the Grand Canal National Cultural Park. Art Obs. 2021, 10, 8–10. [Google Scholar]
Type of Heritage | Sub-Item | |
---|---|---|
Material Cultural Heritage of the Chinese Grand Canal | Canal Hydraulic Engineering Heritage | Course of Canal |
Water | ||
Hydraulic Engineering Facility | ||
Shipping Engineering Facility | ||
Canal Authority | ||
Associated Historical Remains | Ancient Ruins | |
Ancient Tomb | ||
Ancient Architecture | ||
Rock Carving | ||
Important Historical Sites and Representative Buildings in Modern Times | ||
Historic Districts and Villages | ||
Intangible Cultural Heritage of the Chinese Grand Canal | Local Name | |
Folk Legend | ||
Folk Music and Traditional Theater | ||
Folk Art and Traditional Crafts | ||
Folklore | ||
Canal Poetry | ||
Associated Environmental Landscapes | Canal Countryside Landscape Setting | |
Canal Townscape Environment |
Label | Label | Label |
---|---|---|
Tree | Catering | Moon Hole Door |
Lighting | Landscape Facility | Scenic Stone |
Grouping Trees | Signage | Pagoda |
Canal | Residence | Artistic Window |
Ancient Architecture | Rockery | Grass |
Ancient Pavilion | Flower | Shrubs |
Store | Bridge | Gate Tower |
Modern Architecture | Boat | Detailed Structure |
Railing | Paving | Memorial Archway |
Revetment | Fence | Sluice |
Craft | Alley | Pond |
Night Sky | Street Lamp | Bonsai |
Label | Frequency | Proportion | Label | Frequency | Proportion |
---|---|---|---|---|---|
Tree | 2711 | 14.09% | Bridge | 344 | 1.79% |
Lighting | 2083 | 10.82% | Boat | 326 | 1.69% |
Grouping Trees | 1938 | 10.07% | Paving | 312 | 1.62% |
Canal | 1432 | 7.44% | Fence | 302 | 1.57% |
Ancient Architecture | 1363 | 7.08% | Alley | 275 | 1.43% |
Ancient Pavilion | 700 | 3.64% | Street Lamp | 271 | 1.41% |
Store | 607 | 3.15% | Moon Hole Door | 255 | 1.32% |
Modern Architecture | 559 | 2.90% | Scenic Stone | 237 | 1.23% |
Railing | 551 | 2.86% | Pagoda | 231 | 1.20% |
Revetment | 486 | 2.53% | Artistic Window | 211 | 1.10% |
Craft | 485 | 2.52% | Grass | 177 | 0.92% |
Night Sky | 409 | 2.13% | Shrubs | 158 | 0.82% |
Catering | 406 | 2.11% | Gate Tower | 154 | 0.80% |
Landscape Facility | 381 | 1.98% | Detailed Structure | 126 | 0.65% |
Signage | 381 | 1.98% | Memorial Archway | 113 | 0.59% |
Residence | 361 | 1.88% | Sluice | 76 | 0.39% |
Rockery | 355 | 1.84% | Pond | 73 | 0.38% |
Flower | 352 | 1.83% | Bonsai | 46 | 0.24% |
Landscape Type | Frequency | Proportion |
---|---|---|
Canal and Watercraft Remains | 2031 | 26.72% |
Classical Garden | 1613 | 21.22% |
Architecture and Settlement | 1564 | 20.58% |
Natural Landscape | 1490 | 19.61% |
Culture and Life | 902 | 11.87% |
Landscape Types | Number of Nodes | Number of Edges | Network Density | Average Clustering Coefficient | Average Shortest Path |
---|---|---|---|---|---|
Canal and Watercraft Remains | 21 | 93 | 0443 | 0.789 | 1.576 |
Classical Garden | 23 | 89 | 0.352 | 0.745 | 1.719 |
Architecture and Settlement | 22 | 70 | 0.303 | 0.57 | 1.861 |
Natural Landscape | 12 | 41 | 0.621 | 0.836 | 1.379 |
Culture and Life | 15 | 32 | 0.305 | 0.578 | 1.848 |
Number of Labels | Co-Occurrence Networks of Labels | Example Photos | |
---|---|---|---|
Canal and Watercraft Remains | Grouping Trees (1140) Canal (1031) Lighting (769) Tree (604) Modern Architecture (461) Ancient Architecture (440) Railing (358) Revetment (329) Boat (263) Ancient Pavilion (235) Street Lamp (202) Night Sky (194) Bridge (191) Paving (172) Residence (148) Pagoda (111) Sluice (76) Fence (74) Landscape Facility (67) Grass (60) Memorial Archway (58) | ||
Classical Garden | Tree (1003) Ancient Architecture (597) Ancient Pavilion (354) Grouping Trees (340) Rockery (313) Canal (221) Moon Hole Door (198) Artistic Window (145) Landscape Facility (137) Flower (136) Lighting (120) Railing (103) Scenic Stone (101) Revetment (99) Bridge (97) Shrubs (75) Pond (73) Fence (69) Paving (65) Boat (63) Pagoda (54) Gate Tower (33) Night Sky (31) | ||
Architecture and Settlement | Lighting (977) Store (511) Ancient Architecture (326) Tree (242) Alley (219) Residence (213) Night Sky (184) Detailed Structure (136) Gate Tower (121) Ancient Pavilion (111) Fence (80) Paving (75) Landscape Facility (67) Pagoda (66) Artistic Window (66) Signage (60) Moon Hole Door (57) Memorial Archway (55) Grouping Trees (49) Rockery (42) Flower (36) Modern Architecture (31) | ||
Natural Landscape | Tree (766) Grouping Trees (370) Canal (180) Flower (137) Grass (117) Scenic Stone (94) Railing (91) Shrubs (83) Street Lamp (69) Modern Architecture (67) Revetment (58) Bridge (56) | ||
Culture and Life | Craft (485) Catering (406) Signage (321) Lighting (217) Detailed Structure (126) Landscape Facility (110) Tree (96) Store (96) Fence (79) Railing (71) Alley (56) Gate Tower (56) Bonsai (46) Flower (43) Scenic Stone (42) Grouping Trees (39) |
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Li, Y.; Qiu, B. Using Deep Learning Approaches to Quantify Landscape Preference of the Chinese Grand Canal: An Empirical Case Study of the Yangzhou Ancient Canal. Sustainability 2024, 16, 3602. https://doi.org/10.3390/su16093602
Li Y, Qiu B. Using Deep Learning Approaches to Quantify Landscape Preference of the Chinese Grand Canal: An Empirical Case Study of the Yangzhou Ancient Canal. Sustainability. 2024; 16(9):3602. https://doi.org/10.3390/su16093602
Chicago/Turabian StyleLi, Yiwen, and Bing Qiu. 2024. "Using Deep Learning Approaches to Quantify Landscape Preference of the Chinese Grand Canal: An Empirical Case Study of the Yangzhou Ancient Canal" Sustainability 16, no. 9: 3602. https://doi.org/10.3390/su16093602