Intentional Characteristics and Public Perceived Preferences of Lake Parks Based on Machine Learning Models
This research aimed to analyze and understand the perceived landscape preferences of lake parks (LPs) and how the public perceives and prefers these elements within the context of lake parks. The objective was to provide insights beneficial for landscape design, urban planning, and the creation of m...
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| Format: | Article |
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MDPI AG
2024-01-01
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| Series: | Land |
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| Online Access: | https://www.mdpi.com/2073-445X/13/1/57 |
| _version_ | 1827371732704428032 |
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| author | Dandan Wang Hyun Min Donggen Rui |
| author_facet | Dandan Wang Hyun Min Donggen Rui |
| author_sort | Dandan Wang |
| collection | DOAJ |
| description | This research aimed to analyze and understand the perceived landscape preferences of lake parks (LPs) and how the public perceives and prefers these elements within the context of lake parks. The objective was to provide insights beneficial for landscape design, urban planning, and the creation of more appealing and sustainable lake parks. To achieve this, two primary methods were employed in this study: the Automated Machine Learning (Auto ML) model and the DeepLab v3+ model. To gather data for the research, 46,444 images were collected from 20 different lake parks from 2019 to 2022. Social media platforms such as Instagram, Flickr, and specific lake park community groups were tapped to source photographs from both professional photographers and the general public. According to the experimental findings, the perceived frequency of natural landscapes was 69.27%, which was higher than that of humanistic landscapes by 30.73%. The perceived intensity was also maintained between 0.09 and 0.25. The perceived frequency of water body landscapes was much greater on a macro-scale, at 73.02%, and the public had various plant preferences throughout the year. Aquatic plant landscapes with low-to-medium green visibility were preferred by the public, according to the landscape share characterization, while amusement rides with medium-to-high openness were preferred. The sky visibility of amusement rides was between 0 and 0.1 and between 0.3 and 0.5, indicating that the public preferred amusement rides with medium-to-high openness. In lake parks, the populace chose settings with less obvious architectural features. When combined, the two models used in this study are useful for identifying and analyzing the intended traits and preferences of lake parks among the general public. They also have theoretical and practical application value for directing the development of lake parks and urban landscapes. |
| first_indexed | 2024-03-08T10:44:50Z |
| format | Article |
| id | doaj.art-f32f6e17013d45b8beac8ba2937ca4e6 |
| institution | Directory Open Access Journal |
| issn | 2073-445X |
| language | English |
| last_indexed | 2024-03-08T10:44:50Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj.art-f32f6e17013d45b8beac8ba2937ca4e62024-01-26T17:19:54ZengMDPI AGLand2073-445X2024-01-011315710.3390/land13010057Intentional Characteristics and Public Perceived Preferences of Lake Parks Based on Machine Learning ModelsDandan Wang0Hyun Min1Donggen Rui2Department of Global and Area Studies, Pukyong National University, 45, Yongso-ro, Nam-gu, Busan 48513, Republic of KoreaDepartment of Global and Area Studies, Pukyong National University, 45, Yongso-ro, Nam-gu, Busan 48513, Republic of KoreaDepartment of Global and Area Studies, Pukyong National University, 45, Yongso-ro, Nam-gu, Busan 48513, Republic of KoreaThis research aimed to analyze and understand the perceived landscape preferences of lake parks (LPs) and how the public perceives and prefers these elements within the context of lake parks. The objective was to provide insights beneficial for landscape design, urban planning, and the creation of more appealing and sustainable lake parks. To achieve this, two primary methods were employed in this study: the Automated Machine Learning (Auto ML) model and the DeepLab v3+ model. To gather data for the research, 46,444 images were collected from 20 different lake parks from 2019 to 2022. Social media platforms such as Instagram, Flickr, and specific lake park community groups were tapped to source photographs from both professional photographers and the general public. According to the experimental findings, the perceived frequency of natural landscapes was 69.27%, which was higher than that of humanistic landscapes by 30.73%. The perceived intensity was also maintained between 0.09 and 0.25. The perceived frequency of water body landscapes was much greater on a macro-scale, at 73.02%, and the public had various plant preferences throughout the year. Aquatic plant landscapes with low-to-medium green visibility were preferred by the public, according to the landscape share characterization, while amusement rides with medium-to-high openness were preferred. The sky visibility of amusement rides was between 0 and 0.1 and between 0.3 and 0.5, indicating that the public preferred amusement rides with medium-to-high openness. In lake parks, the populace chose settings with less obvious architectural features. When combined, the two models used in this study are useful for identifying and analyzing the intended traits and preferences of lake parks among the general public. They also have theoretical and practical application value for directing the development of lake parks and urban landscapes.https://www.mdpi.com/2073-445X/13/1/57auto ML modelinglake parksimagery featurespublic perceived preferences |
| spellingShingle | Dandan Wang Hyun Min Donggen Rui Intentional Characteristics and Public Perceived Preferences of Lake Parks Based on Machine Learning Models Land auto ML modeling lake parks imagery features public perceived preferences |
| title | Intentional Characteristics and Public Perceived Preferences of Lake Parks Based on Machine Learning Models |
| title_full | Intentional Characteristics and Public Perceived Preferences of Lake Parks Based on Machine Learning Models |
| title_fullStr | Intentional Characteristics and Public Perceived Preferences of Lake Parks Based on Machine Learning Models |
| title_full_unstemmed | Intentional Characteristics and Public Perceived Preferences of Lake Parks Based on Machine Learning Models |
| title_short | Intentional Characteristics and Public Perceived Preferences of Lake Parks Based on Machine Learning Models |
| title_sort | intentional characteristics and public perceived preferences of lake parks based on machine learning models |
| topic | auto ML modeling lake parks imagery features public perceived preferences |
| url | https://www.mdpi.com/2073-445X/13/1/57 |
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