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Decision-Level and Feature-Level Integration of Remote Sensing and Geospatial Big Data for Urban Land Use Mapping
Yin, Jiadi1,2,3; Fu, Ping1,2; Hamm, Nicholas A. S.1,2; Li, Zhichao3; You, Nanshan3,4; He, Yingli3,4; Cheshmehzangi, Ali5; Dong, Jinwei3
2021-04-01
Source PublicationREMOTE SENSING
Volume13Issue:8Pages:17
Corresponding AuthorFu, Ping(Ping.Fu@nottingham.edu.cn)
AbstractInformation about urban land use is important for urban planning and sustainable development. The emergence of geospatial big data (GBD), increased the availability of remotely sensed (RS) data and the development of new methods for data integration to provide new opportunities for mapping types of urban land use. However, the modes of RS and GBD integration are diverse due to the differences in data, study areas, classifiers, etc. In this context, this study aims to summarize the main methods of data integration and evaluate them via a case study of urban land use mapping in Hangzhou, China. We first categorized the RS and GBD integration methods into decision-level integration (DI) and feature-level integration (FI) and analyzed their main differences by reviewing the existing literature. The two methods were then applied for mapping urban land use types in Hangzhou city, based on urban parcels derived from the OpenStreetMap (OSM) road network, 10 m Sentinel-2A images, and points of interest (POI). The corresponding classification results were validated quantitatively and qualitatively using the same testing dataset. Finally, we illustrated the advantages and disadvantages of both approaches via bibliographic evidence and quantitative analysis. The results showed that: (1) The visual comparison indicates a generally better performance of DI-based classification than FI-based classification; (2) DI-based urban land use mapping is easy to implement, while FI-based land use mapping enables the mixture of features; (3) DI-based and FI-based methods can be used together to improve urban land use mapping, as they have different performances when classifying different types of land use. This study provides an improved understanding of urban land use mapping in terms of the RS and GBD integration strategy.
Keywordurban land use remote sensing geospatial big data decision-level integration feature-level integration Hangzhou
DOI10.3390/rs13081579
WOS KeywordGOOGLE EARTH ENGINE ; TIME-SERIES ; SOCIAL-MEDIA ; USE CLASSIFICATION ; FUNCTIONAL ZONES ; COVER ; IMAGERY ; AREA ; SURFACE ; POPULATION
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41971078] ; National Natural Science Foundation of China[41871349] ; National Natural Science Foundation of China[41801336] ; Chinese Academy of Sciences the Strategic Priority Research Program[XDA19040301] ; Key Research Program of Frontier Sciences[QYZDB-SSW-DQC005]
Funding OrganizationNational Natural Science Foundation of China ; Chinese Academy of Sciences the Strategic Priority Research Program ; Key Research Program of Frontier Sciences
WOS Research AreaEnvironmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectEnvironmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000644671100001
PublisherMDPI
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/161641
Collection中国科学院地理科学与资源研究所
Corresponding AuthorFu, Ping
Affiliation1.Univ Nottingham Ningbo China, Sch Geog Sci, Fac Sci & Engn, Ningbo 315100, Peoples R China
2.Univ Nottingham Ningbo China, Geospatial & Geohazards Res Grp, Fac Sci & Engn, Ningbo 315100, Peoples R China
3.Chinese Acad Sci, Key Lab Land Surface Pattern & Simulat, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
5.Univ Nottingham Ningbo China, Dept Architecture & Built Environm, Ningbo 315100, Peoples R China
Recommended Citation
GB/T 7714
Yin, Jiadi,Fu, Ping,Hamm, Nicholas A. S.,et al. Decision-Level and Feature-Level Integration of Remote Sensing and Geospatial Big Data for Urban Land Use Mapping[J]. REMOTE SENSING,2021,13(8):17.
APA Yin, Jiadi.,Fu, Ping.,Hamm, Nicholas A. S..,Li, Zhichao.,You, Nanshan.,...&Dong, Jinwei.(2021).Decision-Level and Feature-Level Integration of Remote Sensing and Geospatial Big Data for Urban Land Use Mapping.REMOTE SENSING,13(8),17.
MLA Yin, Jiadi,et al."Decision-Level and Feature-Level Integration of Remote Sensing and Geospatial Big Data for Urban Land Use Mapping".REMOTE SENSING 13.8(2021):17.
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