Inferring Mixed Use of Buildings with Multisource Data Based on Tensor Decomposition
Information on the mixed use of buildings helps understand the status of mixed-use urban vertical land and assists in urban planning decisions. Although a few studies have focused on this topic, the methods they used are quite complex and require manual intervention in extracting different function...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/10/3/185 |
| _version_ | 1797540540903849984 |
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| author | Chenyang Zhang Qingli Shi Li Zhuo Fang Wang Haiyan Tao |
| author_facet | Chenyang Zhang Qingli Shi Li Zhuo Fang Wang Haiyan Tao |
| author_sort | Chenyang Zhang |
| collection | DOAJ |
| description | Information on the mixed use of buildings helps understand the status of mixed-use urban vertical land and assists in urban planning decisions. Although a few studies have focused on this topic, the methods they used are quite complex and require manual intervention in extracting different function patterns of buildings, while building recognition rates remain unsatisfying. In this paper, we propose a new method to infer the mixed use of buildings based on a tensor decomposition algorithm, which integrates information from both high-resolution remote sensing images and social sensing data. We selected the Tianhe District of Guangzhou, China to validate our method. The results show that the recognition rate of buildings can reach 98.67%, with an average recognition accuracy of 84%. Our study proves that the tensor decomposition algorithm can extract different function patterns of buildings unsupervised, while remote sensing data can provide key information for inferring building functions. The tensor decomposition-based method can serve as an effective and efficient way to infer the mixed use of buildings, which can achieve better results with simpler steps. |
| first_indexed | 2024-03-10T13:02:39Z |
| format | Article |
| id | doaj.art-1e9252d32f1c454fa74d416958b5afd9 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T13:02:39Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-1e9252d32f1c454fa74d416958b5afd92023-11-21T11:21:03ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-03-0110318510.3390/ijgi10030185Inferring Mixed Use of Buildings with Multisource Data Based on Tensor DecompositionChenyang Zhang0Qingli Shi1Li Zhuo2Fang Wang3Haiyan Tao4Center of Integrated Geographic Information Analaysis, School of Geography Science and Planning, Sun Yat-sen University, Guangzhou 510275, ChinaCenter of Integrated Geographic Information Analaysis, School of Geography Science and Planning, Sun Yat-sen University, Guangzhou 510275, ChinaCenter of Integrated Geographic Information Analaysis, School of Geography Science and Planning, Sun Yat-sen University, Guangzhou 510275, ChinaSchool of Geographical Science, Guangzhou University, Guangzhou 510006, ChinaCenter of Integrated Geographic Information Analaysis, School of Geography Science and Planning, Sun Yat-sen University, Guangzhou 510275, ChinaInformation on the mixed use of buildings helps understand the status of mixed-use urban vertical land and assists in urban planning decisions. Although a few studies have focused on this topic, the methods they used are quite complex and require manual intervention in extracting different function patterns of buildings, while building recognition rates remain unsatisfying. In this paper, we propose a new method to infer the mixed use of buildings based on a tensor decomposition algorithm, which integrates information from both high-resolution remote sensing images and social sensing data. We selected the Tianhe District of Guangzhou, China to validate our method. The results show that the recognition rate of buildings can reach 98.67%, with an average recognition accuracy of 84%. Our study proves that the tensor decomposition algorithm can extract different function patterns of buildings unsupervised, while remote sensing data can provide key information for inferring building functions. The tensor decomposition-based method can serve as an effective and efficient way to infer the mixed use of buildings, which can achieve better results with simpler steps.https://www.mdpi.com/2220-9964/10/3/185mixed-use buildingmultisource datatensor decomposition |
| spellingShingle | Chenyang Zhang Qingli Shi Li Zhuo Fang Wang Haiyan Tao Inferring Mixed Use of Buildings with Multisource Data Based on Tensor Decomposition ISPRS International Journal of Geo-Information mixed-use building multisource data tensor decomposition |
| title | Inferring Mixed Use of Buildings with Multisource Data Based on Tensor Decomposition |
| title_full | Inferring Mixed Use of Buildings with Multisource Data Based on Tensor Decomposition |
| title_fullStr | Inferring Mixed Use of Buildings with Multisource Data Based on Tensor Decomposition |
| title_full_unstemmed | Inferring Mixed Use of Buildings with Multisource Data Based on Tensor Decomposition |
| title_short | Inferring Mixed Use of Buildings with Multisource Data Based on Tensor Decomposition |
| title_sort | inferring mixed use of buildings with multisource data based on tensor decomposition |
| topic | mixed-use building multisource data tensor decomposition |
| url | https://www.mdpi.com/2220-9964/10/3/185 |
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