Exploring Impact of Spatial Unit on Urban Land Use Mapping with Multisource Data
The ability to precisely map urban land use types can significantly aid urban planning and urban system understanding. In recent years, remote sensing images and social sensing data have been frequently used for urban land use mapping. However, there still remains a problem: what is the best basic u...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/21/3597 |
| _version_ | 1797549068862357504 |
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| author | Xuanyan Dong Yue Xu Leping Huang Zhigang Liu Yi Xu Kangyong Zhang Zhongwen Hu Guofeng Wu |
| author_facet | Xuanyan Dong Yue Xu Leping Huang Zhigang Liu Yi Xu Kangyong Zhang Zhongwen Hu Guofeng Wu |
| author_sort | Xuanyan Dong |
| collection | DOAJ |
| description | The ability to precisely map urban land use types can significantly aid urban planning and urban system understanding. In recent years, remote sensing images and social sensing data have been frequently used for urban land use mapping. However, there still remains a problem: what is the best basic unit for fusing remote sensing images with social sensing data? The aim of this study is to explore the impact of spatial units on urban land use mapping, with remote sensing images and social sensing data of Shenzhen City, China. Three different basic units were first applied to delineate urban land use types, and for each unit, a word dictionary was built by fusing natural–physical features from high spatial resolution (HSR) remote sensing images and the socioeconomic semantic features from point of interest (POI) data. The latent Dirichlet allocation (LDA) algorithm and random forest methods were then applied to map the land use of the Futian district—the core region of Shenzhen. The experiment demonstrates that: (1) No matter what kind of spatial unit, it is beneficial to fuse multisource data to improve the performance. However, when using different spatial units, the importances of features are different. (2) Using block-based spatial units results in the final map looking the best. However, a great challenge of this approach is that the scale is too coarse to handle mixed functional areas. (3) Using grid- and object-based units, the problem of mixed functional areas can be better solved. Additionally, the object-based land use map looks better from our visual interpretation. Accordingly, the results of this study could give other researchers references and advice for future studies. |
| first_indexed | 2024-03-10T15:09:26Z |
| format | Article |
| id | doaj.art-d49e6c3e64ba45e58b2a28e600918536 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T15:09:26Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d49e6c3e64ba45e58b2a28e6009185362023-11-20T19:31:24ZengMDPI AGRemote Sensing2072-42922020-11-011221359710.3390/rs12213597Exploring Impact of Spatial Unit on Urban Land Use Mapping with Multisource DataXuanyan Dong0Yue Xu1Leping Huang2Zhigang Liu3Yi Xu4Kangyong Zhang5Zhongwen Hu6Guofeng Wu7MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area and Guangdong Key Laboratory of Urban Informatics and Shenzhen Key Laboratory of Spatial Smart Sensing and Services and Research Institute for Smart Cities, Shenzhen University, Shenzhen 518060, ChinaMNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area and Guangdong Key Laboratory of Urban Informatics and Shenzhen Key Laboratory of Spatial Smart Sensing and Services and Research Institute for Smart Cities, Shenzhen University, Shenzhen 518060, ChinaMNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area and Guangdong Key Laboratory of Urban Informatics and Shenzhen Key Laboratory of Spatial Smart Sensing and Services and Research Institute for Smart Cities, Shenzhen University, Shenzhen 518060, ChinaMNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area and Guangdong Key Laboratory of Urban Informatics and Shenzhen Key Laboratory of Spatial Smart Sensing and Services and Research Institute for Smart Cities, Shenzhen University, Shenzhen 518060, ChinaMNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area and Guangdong Key Laboratory of Urban Informatics and Shenzhen Key Laboratory of Spatial Smart Sensing and Services and Research Institute for Smart Cities, Shenzhen University, Shenzhen 518060, ChinaMNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area and Guangdong Key Laboratory of Urban Informatics and Shenzhen Key Laboratory of Spatial Smart Sensing and Services and Research Institute for Smart Cities, Shenzhen University, Shenzhen 518060, ChinaMNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area and Guangdong Key Laboratory of Urban Informatics and Shenzhen Key Laboratory of Spatial Smart Sensing and Services and Research Institute for Smart Cities, Shenzhen University, Shenzhen 518060, ChinaMNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area and Guangdong Key Laboratory of Urban Informatics and Shenzhen Key Laboratory of Spatial Smart Sensing and Services and Research Institute for Smart Cities, Shenzhen University, Shenzhen 518060, ChinaThe ability to precisely map urban land use types can significantly aid urban planning and urban system understanding. In recent years, remote sensing images and social sensing data have been frequently used for urban land use mapping. However, there still remains a problem: what is the best basic unit for fusing remote sensing images with social sensing data? The aim of this study is to explore the impact of spatial units on urban land use mapping, with remote sensing images and social sensing data of Shenzhen City, China. Three different basic units were first applied to delineate urban land use types, and for each unit, a word dictionary was built by fusing natural–physical features from high spatial resolution (HSR) remote sensing images and the socioeconomic semantic features from point of interest (POI) data. The latent Dirichlet allocation (LDA) algorithm and random forest methods were then applied to map the land use of the Futian district—the core region of Shenzhen. The experiment demonstrates that: (1) No matter what kind of spatial unit, it is beneficial to fuse multisource data to improve the performance. However, when using different spatial units, the importances of features are different. (2) Using block-based spatial units results in the final map looking the best. However, a great challenge of this approach is that the scale is too coarse to handle mixed functional areas. (3) Using grid- and object-based units, the problem of mixed functional areas can be better solved. Additionally, the object-based land use map looks better from our visual interpretation. Accordingly, the results of this study could give other researchers references and advice for future studies.https://www.mdpi.com/2072-4292/12/21/3597urban land use mappingLDArandom forestremote sensingPOIurban functional area |
| spellingShingle | Xuanyan Dong Yue Xu Leping Huang Zhigang Liu Yi Xu Kangyong Zhang Zhongwen Hu Guofeng Wu Exploring Impact of Spatial Unit on Urban Land Use Mapping with Multisource Data Remote Sensing urban land use mapping LDA random forest remote sensing POI urban functional area |
| title | Exploring Impact of Spatial Unit on Urban Land Use Mapping with Multisource Data |
| title_full | Exploring Impact of Spatial Unit on Urban Land Use Mapping with Multisource Data |
| title_fullStr | Exploring Impact of Spatial Unit on Urban Land Use Mapping with Multisource Data |
| title_full_unstemmed | Exploring Impact of Spatial Unit on Urban Land Use Mapping with Multisource Data |
| title_short | Exploring Impact of Spatial Unit on Urban Land Use Mapping with Multisource Data |
| title_sort | exploring impact of spatial unit on urban land use mapping with multisource data |
| topic | urban land use mapping LDA random forest remote sensing POI urban functional area |
| url | https://www.mdpi.com/2072-4292/12/21/3597 |
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