Intelligent Recognition and 3D Modeling of Rural Buildings Based on Multi-Source Data Fusion
China's rural areas are vast, and housing construction is the primary organization of farmers' living spaces and an essential focus of the national implementation of rural revitalization. However, there is a lack of rural housing census data and methods that quickly and accurately establis...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Committee of Tropical Geography
2023-02-01
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| Series: | Redai dili |
| Subjects: | |
| Online Access: | http://www.rddl.com.cn/CN/10.13284/j.cnki.rddl.003633 |
| _version_ | 1811158992446029824 |
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| author | Chen Biao Peng Xinyue Zhou Suhong Chen Jialiang Kong Xianjuan Bian Mingyue Lin Gaoyuan |
| author_facet | Chen Biao Peng Xinyue Zhou Suhong Chen Jialiang Kong Xianjuan Bian Mingyue Lin Gaoyuan |
| author_sort | Chen Biao |
| collection | DOAJ |
| description | China's rural areas are vast, and housing construction is the primary organization of farmers' living spaces and an essential focus of the national implementation of rural revitalization. However, there is a lack of rural housing census data and methods that quickly and accurately establish a rural three-dimensional (3D)-building model. The existing 3D-building modeling techniques, including manual modeling and oblique photography modeling, are encountering the problem of high cost and do not meet the construction requirements of low cost and comprehensive coverage. With the development of satellite remote sensing technology in China, building recognition based on high-resolution remote sensing images has become a convenient and rapid technical tool. At the same time, with the widespread use of smartphones and their potent computing power, people can easily and quickly access the Internet and receive a three-dimensional display. Compared with two-dimensional products, three-dimensional products can show rural buildings, terrain, and landscape more clearly, enhance the refined management of rural areas, and improve enthusiasm to participate in rural construction. Therefore, this study proposes a simple rural 3D building modeling method based on multi-source data fusion, namely intelligent identification and 3D modeling for rural buildings. This method consists of two stages: rough model generation and deepening. In the rough model generation stage, the building is identified based on high-resolution remote sensing images and Mask R_CNN technology, the location of the building is determined, and the basic white model is obtained by stretching. In the deepening stage, field collectors replace the basic white model with a more refined and parameterized model based on the rural building model library, according to the actual situation. Subsequently, they supplement the building facade texture through smartphone photography and texture processing. Finally, a physical, storable, and exchangeable 3D-building model is obtained through coordinate matching, image terrain fusion, 3D-lightweight technology, and other technologies. This study adopts the modeling strategy of gradual deepening to reduce the modeling cost. The associated high-resolution remote sensing image recognition technology and mobile phone-based 3D modeling and display technologies are relatively advanced. Based on the characteristics of architectural styles in rural areas, a set of rural building-model libraries based on CSG technology was constructed. Model replacement, model size adjustment, texture mapping, and other operations were quickly used to build a refined 3D model. Finally, the 3D models were lightly processed and fused with the image topography and other data, which meets the demand for smooth browsing from various aspects. The 3D model of Hecun Village in Xinxing County was experimentally reconstructed, illustrating that the method can support applications such as rural surveys, rural planning, rural construction, and co-production. The modeling results show that the method is simple, easy to use, and reduces the high requirements of conventional modeling in terms of data acquisition and processing. It can provide a low-cost, highly efficient, and prevalent 3D-reconstruction method for rural regions, which is suitable for widespread promotion. |
| first_indexed | 2024-04-10T05:33:36Z |
| format | Article |
| id | doaj.art-94b332bba18e4e67af7793404f371906 |
| institution | Directory Open Access Journal |
| issn | 1001-5221 |
| language | zho |
| last_indexed | 2024-04-10T05:33:36Z |
| publishDate | 2023-02-01 |
| publisher | Editorial Committee of Tropical Geography |
| record_format | Article |
| series | Redai dili |
| spelling | doaj.art-94b332bba18e4e67af7793404f3719062023-03-07T01:17:30ZzhoEditorial Committee of Tropical GeographyRedai dili1001-52212023-02-0143219020110.13284/j.cnki.rddl.0036331001-5221(2023)02-0190-12Intelligent Recognition and 3D Modeling of Rural Buildings Based on Multi-Source Data FusionChen Biao0Peng Xinyue1Zhou Suhong2Chen Jialiang3Kong Xianjuan4Bian Mingyue5Lin Gaoyuan6Augur Technology Co., Ltd., Guangzhou 510000, ChinaAugur Technology Co., Ltd., Guangzhou 510000, ChinaSchool of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, ChinaAugur Technology Co., Ltd., Guangzhou 510000, ChinaAugur Technology Co., Ltd., Guangzhou 510000, ChinaAugur Technology Co., Ltd., Guangzhou 510000, ChinaAugur Technology Co., Ltd., Guangzhou 510000, ChinaChina's rural areas are vast, and housing construction is the primary organization of farmers' living spaces and an essential focus of the national implementation of rural revitalization. However, there is a lack of rural housing census data and methods that quickly and accurately establish a rural three-dimensional (3D)-building model. The existing 3D-building modeling techniques, including manual modeling and oblique photography modeling, are encountering the problem of high cost and do not meet the construction requirements of low cost and comprehensive coverage. With the development of satellite remote sensing technology in China, building recognition based on high-resolution remote sensing images has become a convenient and rapid technical tool. At the same time, with the widespread use of smartphones and their potent computing power, people can easily and quickly access the Internet and receive a three-dimensional display. Compared with two-dimensional products, three-dimensional products can show rural buildings, terrain, and landscape more clearly, enhance the refined management of rural areas, and improve enthusiasm to participate in rural construction. Therefore, this study proposes a simple rural 3D building modeling method based on multi-source data fusion, namely intelligent identification and 3D modeling for rural buildings. This method consists of two stages: rough model generation and deepening. In the rough model generation stage, the building is identified based on high-resolution remote sensing images and Mask R_CNN technology, the location of the building is determined, and the basic white model is obtained by stretching. In the deepening stage, field collectors replace the basic white model with a more refined and parameterized model based on the rural building model library, according to the actual situation. Subsequently, they supplement the building facade texture through smartphone photography and texture processing. Finally, a physical, storable, and exchangeable 3D-building model is obtained through coordinate matching, image terrain fusion, 3D-lightweight technology, and other technologies. This study adopts the modeling strategy of gradual deepening to reduce the modeling cost. The associated high-resolution remote sensing image recognition technology and mobile phone-based 3D modeling and display technologies are relatively advanced. Based on the characteristics of architectural styles in rural areas, a set of rural building-model libraries based on CSG technology was constructed. Model replacement, model size adjustment, texture mapping, and other operations were quickly used to build a refined 3D model. Finally, the 3D models were lightly processed and fused with the image topography and other data, which meets the demand for smooth browsing from various aspects. The 3D model of Hecun Village in Xinxing County was experimentally reconstructed, illustrating that the method can support applications such as rural surveys, rural planning, rural construction, and co-production. The modeling results show that the method is simple, easy to use, and reduces the high requirements of conventional modeling in terms of data acquisition and processing. It can provide a low-cost, highly efficient, and prevalent 3D-reconstruction method for rural regions, which is suitable for widespread promotion.http://www.rddl.com.cn/CN/10.13284/j.cnki.rddl.003633rural modelingbuilding recognitiontexture mappingmask r_cnn |
| spellingShingle | Chen Biao Peng Xinyue Zhou Suhong Chen Jialiang Kong Xianjuan Bian Mingyue Lin Gaoyuan Intelligent Recognition and 3D Modeling of Rural Buildings Based on Multi-Source Data Fusion Redai dili rural modeling building recognition texture mapping mask r_cnn |
| title | Intelligent Recognition and 3D Modeling of Rural Buildings Based on Multi-Source Data Fusion |
| title_full | Intelligent Recognition and 3D Modeling of Rural Buildings Based on Multi-Source Data Fusion |
| title_fullStr | Intelligent Recognition and 3D Modeling of Rural Buildings Based on Multi-Source Data Fusion |
| title_full_unstemmed | Intelligent Recognition and 3D Modeling of Rural Buildings Based on Multi-Source Data Fusion |
| title_short | Intelligent Recognition and 3D Modeling of Rural Buildings Based on Multi-Source Data Fusion |
| title_sort | intelligent recognition and 3d modeling of rural buildings based on multi source data fusion |
| topic | rural modeling building recognition texture mapping mask r_cnn |
| url | http://www.rddl.com.cn/CN/10.13284/j.cnki.rddl.003633 |
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