Intelligent approach to mining cultural tourism potential areas based on YOLOv4: insights from Macau
Based on historical background and geopolitical factors, Macau is closely related to Portuguese cities; Macau also currently attaches great importance to the creation of a Sino-Portuguese platform and cultural tourism to drive better economic development. This study explores a method for identifying...
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| Language: | English |
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Taylor & Francis Group
2025-01-01
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| Series: | Journal of Asian Architecture and Building Engineering |
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| Online Access: | http://dx.doi.org/10.1080/13467581.2023.2287211 |
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| author | Yile Chen Lina Yan Liang Zheng |
| author_facet | Yile Chen Lina Yan Liang Zheng |
| author_sort | Yile Chen |
| collection | DOAJ |
| description | Based on historical background and geopolitical factors, Macau is closely related to Portuguese cities; Macau also currently attaches great importance to the creation of a Sino-Portuguese platform and cultural tourism to drive better economic development. This study explores a method for identifying and mining cultural tourism potential areas developed under the YOLOv4 framework and cosine similarity. The main conclusions of this study are as follows: (1) While the YOLOv4 method shows good performance and can effectively identify the architectural texture of Portuguese cities, it may face certain difficulties if applied to newly built urban areas in Macau. (2) Through the cosine similarity method, it is found that the architectural texture slices of the three island cities of Portugal and Macau have certain similarities. The city with the highest level of similarity with Macau is Lisboa, followed by Porto and Evora, while Guimaraes has the lowest level of similarity with Macau. This finding serves as a certain reference for the subsequent construction of the cultural tourism characteristics of the region. (3) Comparing the characteristics of Macau city obtained through the cosine similarity algorithm with the city slices that have a high level of similarity in Portugal, we find the following: “street networks”, “street blocks”, squares, and open space form in the Macau Peninsula, while “street blocks” and “large-scale building complexes” form in the outlying islands. These types of spaces have different scales and can be developed in the future in conjunction with cultural tourism activities on different scales. This study applies machine learning methods to the development of urban cultural tourism potential areas, which provides new perspectives and methods for urban tourism development planning. |
| format | Article |
| id | doaj-art-86b7b0a203f14dbe8521ef9d3a6f413e |
| institution | DOAJ |
| issn | 1347-2852 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Asian Architecture and Building Engineering |
| spelling | doaj-art-86b7b0a203f14dbe8521ef9d3a6f413e2025-08-20T02:57:47ZengTaylor & Francis GroupJournal of Asian Architecture and Building Engineering1347-28522025-01-0124139542310.1080/13467581.2023.22872112287211Intelligent approach to mining cultural tourism potential areas based on YOLOv4: insights from MacauYile Chen0Lina Yan1Liang Zheng2Macau University of Science and TechnologyMacau University of Science and TechnologyMacau University of Science and TechnologyBased on historical background and geopolitical factors, Macau is closely related to Portuguese cities; Macau also currently attaches great importance to the creation of a Sino-Portuguese platform and cultural tourism to drive better economic development. This study explores a method for identifying and mining cultural tourism potential areas developed under the YOLOv4 framework and cosine similarity. The main conclusions of this study are as follows: (1) While the YOLOv4 method shows good performance and can effectively identify the architectural texture of Portuguese cities, it may face certain difficulties if applied to newly built urban areas in Macau. (2) Through the cosine similarity method, it is found that the architectural texture slices of the three island cities of Portugal and Macau have certain similarities. The city with the highest level of similarity with Macau is Lisboa, followed by Porto and Evora, while Guimaraes has the lowest level of similarity with Macau. This finding serves as a certain reference for the subsequent construction of the cultural tourism characteristics of the region. (3) Comparing the characteristics of Macau city obtained through the cosine similarity algorithm with the city slices that have a high level of similarity in Portugal, we find the following: “street networks”, “street blocks”, squares, and open space form in the Macau Peninsula, while “street blocks” and “large-scale building complexes” form in the outlying islands. These types of spaces have different scales and can be developed in the future in conjunction with cultural tourism activities on different scales. This study applies machine learning methods to the development of urban cultural tourism potential areas, which provides new perspectives and methods for urban tourism development planning.http://dx.doi.org/10.1080/13467581.2023.2287211tourism potential areamachine learningcosine similarityportuguese citymacau |
| spellingShingle | Yile Chen Lina Yan Liang Zheng Intelligent approach to mining cultural tourism potential areas based on YOLOv4: insights from Macau Journal of Asian Architecture and Building Engineering tourism potential area machine learning cosine similarity portuguese city macau |
| title | Intelligent approach to mining cultural tourism potential areas based on YOLOv4: insights from Macau |
| title_full | Intelligent approach to mining cultural tourism potential areas based on YOLOv4: insights from Macau |
| title_fullStr | Intelligent approach to mining cultural tourism potential areas based on YOLOv4: insights from Macau |
| title_full_unstemmed | Intelligent approach to mining cultural tourism potential areas based on YOLOv4: insights from Macau |
| title_short | Intelligent approach to mining cultural tourism potential areas based on YOLOv4: insights from Macau |
| title_sort | intelligent approach to mining cultural tourism potential areas based on yolov4 insights from macau |
| topic | tourism potential area machine learning cosine similarity portuguese city macau |
| url | http://dx.doi.org/10.1080/13467581.2023.2287211 |
| work_keys_str_mv | AT yilechen intelligentapproachtominingculturaltourismpotentialareasbasedonyolov4insightsfrommacau AT linayan intelligentapproachtominingculturaltourismpotentialareasbasedonyolov4insightsfrommacau AT liangzheng intelligentapproachtominingculturaltourismpotentialareasbasedonyolov4insightsfrommacau |