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A Multisource Dynamic Fusion Network for Urban Functional Zone Identification on Remote Sensing, POI, and Building Footprint

文献类型：期刊论文

作者Qiao, Hangfeng2; Jiang, Huiping1; Yang, Gang4; Jing, Faming3; Sun, Weiwei4; Lu, Chenyang2; Meng, Xiangchao2
刊名IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
出版日期2024
卷号17页码:10583-10599
关键词Buildings Remote sensing Feature extraction Semantics Soft sensors Land surface Visualization Deep learning (DL) multimodal data fusion remote sensing imagery (RSI) social sensing data urban functional zone (UFZ)
ISSN号1939-1404
DOI10.1109/JSTARS.2024.3404094
英文摘要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.
WOS关键词IMAGES ; CHINA
资助项目Zhejiang Provincial Natural Science Foundation of China
WOS研究方向Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001246279000017
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构Zhejiang Provincial Natural Science Foundation of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/206514]  
专题区域可持续发展分析与模拟院重点实验室_外文论文
通讯作者Meng, Xiangchao
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Reg Sustainable Dev Modeling, Beijing 100101, Peoples R China
2.Ningbo Univ, Fac Elect Engn & Comp Sci, Ningbo 315211, Peoples R China
3.Ningbo Inst Surveying Mapping & Remote Sensing, Ningbo 315211, Peoples R China
4.Ningbo Univ, Dept Geog & Spatial Informat Tech, Ningbo 315211, Peoples R China
推荐引用方式
GB/T 7714		 Qiao, Hangfeng,Jiang, Huiping,Yang, Gang,et al. A Multisource Dynamic Fusion Network for Urban Functional Zone Identification on Remote Sensing, POI, and Building Footprint[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2024,17:10583-10599.
APA Qiao, Hangfeng.,Jiang, Huiping.,Yang, Gang.,Jing, Faming.,Sun, Weiwei.,...&Meng, Xiangchao.(2024).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,17,10583-10599.
MLA Qiao, Hangfeng,et al."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 17(2024):10583-10599.

入库方式： OAI收割

来源：地理科学与资源研究所

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