A Multisource Dynamic Fusion Network for Urban Functional Zone Identification on Remote Sensing, POI, and Building Footprint
Urban functional zones (UFZ) identification with remote sensing imagery (RSI) is attracting increasing attention in urban planning and resource allocation in urban areas, etc. The UFZ is a comprehensive unit comprising geographical, how to effectively integrate the RSI and points of interest (POI) w...
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IEEE
2024-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10542987/ |
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| author | Hangfeng Qiao Huiping Jiang Gang Yang Faming Jing Weiwei Sun Chenyang Lu Xiangchao Meng |
| author_facet | Hangfeng Qiao Huiping Jiang Gang Yang Faming Jing Weiwei Sun Chenyang Lu Xiangchao Meng |
| author_sort | Hangfeng Qiao |
| collection | DOAJ |
| description | Urban functional zones (UFZ) identification with remote sensing imagery (RSI) is attracting increasing attention in urban planning and resource allocation in urban areas, etc. The UFZ is a comprehensive unit comprising geographical, how to effectively integrate the RSI and points of interest (POI) with different physical and socioeconomic characteristics is important and promising. However, there are two challenges for the UFZ identification. On one hand, the UFZ is closely related to buildings, and most current methods lack an in-depth understanding of building semantics. Therefore, an efficient integration of building footprint (FT) data deserves further investigation. On the other hand, these RSI, POI, and FT data are heterogeneous; how to effectively leverage complementary information among these highly heterogeneous modalities to enhance the comprehensive understanding of urban. To solve the above challenges, this article introduces an end-to-end deep learning-based multisource dynamic fusion network for UFZ identification on RSI, POI, and FT. In the proposed method, an adaptive weight interactive fusion module is designed to comprehensively integrate the complementary information among the heterogeneous RSI, POI, and FT data sources. In addition, a multiscale feature focus module is proposed to extract multiscale image features and emphasize critical characteristics. This method was applied to UFZ classification in Ningbo, Zhejiang Province, China, and the experimental results demonstrate the competitive performance. |
| format | Article |
| id | doaj-art-a9ef8eb02a8947b99a01fbe21fcabb09 |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-a9ef8eb02a8947b99a01fbe21fcabb092025-08-20T02:07:23ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352024-01-0117105831059910.1109/JSTARS.2024.340409410542987A Multisource Dynamic Fusion Network for Urban Functional Zone Identification on Remote Sensing, POI, and Building FootprintHangfeng Qiao0https://orcid.org/0009-0009-6506-8958Huiping Jiang1https://orcid.org/0000-0001-9539-1662Gang Yang2https://orcid.org/0000-0002-7001-2037Faming Jing3Weiwei Sun4https://orcid.org/0000-0003-3399-7858Chenyang Lu5https://orcid.org/0000-0002-5565-7689Xiangchao Meng6https://orcid.org/0000-0002-7405-3143Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, ChinaKey Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, ChinaDepartment of Geography and Spatial Information Techniques, Ningbo University, Ningbo, ChinaNingbo Institute of Surveying Mapping and Remote Sensing, Ningbo, ChinaDepartment of Geography and Spatial Information Techniques, Ningbo University, Ningbo, ChinaFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, ChinaFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, ChinaUrban functional zones (UFZ) identification with remote sensing imagery (RSI) is attracting increasing attention in urban planning and resource allocation in urban areas, etc. The UFZ is a comprehensive unit comprising geographical, how to effectively integrate the RSI and points of interest (POI) with different physical and socioeconomic characteristics is important and promising. However, there are two challenges for the UFZ identification. On one hand, the UFZ is closely related to buildings, and most current methods lack an in-depth understanding of building semantics. Therefore, an efficient integration of building footprint (FT) data deserves further investigation. On the other hand, these RSI, POI, and FT data are heterogeneous; how to effectively leverage complementary information among these highly heterogeneous modalities to enhance the comprehensive understanding of urban. To solve the above challenges, this article introduces an end-to-end deep learning-based multisource dynamic fusion network for UFZ identification on RSI, POI, and FT. In the proposed method, an adaptive weight interactive fusion module is designed to comprehensively integrate the complementary information among the heterogeneous RSI, POI, and FT data sources. In addition, a multiscale feature focus module is proposed to extract multiscale image features and emphasize critical characteristics. This method was applied to UFZ classification in Ningbo, Zhejiang Province, China, and the experimental results demonstrate the competitive performance.https://ieeexplore.ieee.org/document/10542987/Deep learning (DL)multimodal data fusionremote sensing imagery (RSI)social sensing dataurban functional zone (UFZ) |
| spellingShingle | Hangfeng Qiao Huiping Jiang Gang Yang Faming Jing Weiwei Sun Chenyang Lu Xiangchao Meng A Multisource Dynamic Fusion Network for Urban Functional Zone Identification on Remote Sensing, POI, and Building Footprint IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Deep learning (DL) multimodal data fusion remote sensing imagery (RSI) social sensing data urban functional zone (UFZ) |
| title | A Multisource Dynamic Fusion Network for Urban Functional Zone Identification on Remote Sensing, POI, and Building Footprint |
| title_full | A Multisource Dynamic Fusion Network for Urban Functional Zone Identification on Remote Sensing, POI, and Building Footprint |
| title_fullStr | A Multisource Dynamic Fusion Network for Urban Functional Zone Identification on Remote Sensing, POI, and Building Footprint |
| title_full_unstemmed | A Multisource Dynamic Fusion Network for Urban Functional Zone Identification on Remote Sensing, POI, and Building Footprint |
| title_short | A Multisource Dynamic Fusion Network for Urban Functional Zone Identification on Remote Sensing, POI, and Building Footprint |
| title_sort | multisource dynamic fusion network for urban functional zone identification on remote sensing poi and building footprint |
| topic | Deep learning (DL) multimodal data fusion remote sensing imagery (RSI) social sensing data urban functional zone (UFZ) |
| url | https://ieeexplore.ieee.org/document/10542987/ |
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