中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 地理科学与资源研究所
  3. 中国科学院地理科学与资源研究所
Integrating remote sensing and geospatial big data for urban land use mapping: A review

文献类型：期刊论文

作者Yin, Jiadi1,3; Dong, Jinwei3; Hamm, Nicholas A. S.1; Li, Zhichao3; Wang, Jianghao5; Xing, Hanfa2,4; Fu, Ping1
刊名INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
出版日期2021-12-01
卷号103页码:11
关键词Integration methods Urban functional zone classification Urban management Land use
ISSN号1569-8432
DOI10.1016/j.jag.2021.102514
通讯作者Dong, Jinwei(dongjw@igsnrr.ac.cn)
英文摘要Remote Sensing (RS) has been used in urban mapping for a long time; however, the complexity and diversity of urban functional patterns are difficult to be captured by RS only. Emerging Geospatial Big Data (GBD) are considered as the supplement to RS data, and help to contribute to our understanding of urban lands from physical aspects (i.e., urban land cover) to socioeconomic aspects (i.e., urban land use). Integrating RS and GBD could be an effective way to combine physical and socioeconomic aspects with great potential for high-quality urban land use classification. In this study, we reviewed the existing literature and focused on the state-of-the-art and perspective of the urban land use categorization by integrating RS and GBD. Specifically, the commonly used RS features (e.g., spectral, textural, temporal, and spatial features) and GBD features (e.g., spatial, temporal, semantic, and sequence features) were identified and analyzed in urban land use classification. The integration strategies for RS and GBD features were categorized into feature-level integration (FI) and decision-level integration (DI). To be more specific, the FI method integrates the RS and GBD features and classifies urban land use types using the integrated feature sets; the DI method processes RS and GBD independently and then merges the classification results based on decision rules. We also discussed other critical issues, including analysis unit setting, parcel segmentation, parcel labeling of land use types, and data integration. Our findings provide a retrospect of different features from RS and GBD, strategies of RS and GBD integration, and their pros and cons, which could help to define the framework for future urban land use mapping and better support urban planning, urban environment assessment, urban disaster monitoring and urban traffic analysis.
WOS关键词SOCIAL-MEDIA DATA ; GOOGLE EARTH ENGINE ; COVER CLASSIFICATION ; IMPERVIOUS SURFACE ; TIME-SERIES ; IMAGERY ; METRICS ; TWITTER ; REGION ; AREAS
资助项目Key Research Program of Frontier Sciences[QYZDB-SSW-DQC005] ; Chinese Academy of Sciences (CAS)[XDA19040301] ; National Natural Science Foundation of China[41871349] ; National Natural Science Foundation of China[41801336] ; National Natural Science Foundation of China[41971078] ; University of Nottingham Ningbo China ; Institute for Geographic Science and Natural Resources Research (IGSNRR), CAS
WOS研究方向Remote Sensing
语种英语
WOS记录号WOS:000696914600001
出版者ELSEVIER
资助机构Key Research Program of Frontier Sciences ; Chinese Academy of Sciences (CAS) ; National Natural Science Foundation of China ; University of Nottingham Ningbo China ; Institute for Geographic Science and Natural Resources Research (IGSNRR), CAS
源URL[http://ir.igsnrr.ac.cn/handle/311030/165678]  
专题中国科学院地理科学与资源研究所
通讯作者Dong, Jinwei
作者单位1.Univ Nottingham, Sch Geog Sci & Geospatial Res Grp, Fac Sci & Engn, Ningbo 315100, Peoples R China
2.South China Normal Univ, Sch Geog, Guangzhou 510631, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China
4.Shandong Normal Univ, Coll Geog & Environm, Jinan 250300, Peoples R China
5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Yin, Jiadi,Dong, Jinwei,Hamm, Nicholas A. S.,et al. Integrating remote sensing and geospatial big data for urban land use mapping: A review[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2021,103:11.
APA Yin, Jiadi.,Dong, Jinwei.,Hamm, Nicholas A. S..,Li, Zhichao.,Wang, Jianghao.,...&Fu, Ping.(2021).Integrating remote sensing and geospatial big data for urban land use mapping: A review.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,103,11.
MLA Yin, Jiadi,et al."Integrating remote sensing and geospatial big data for urban land use mapping: A review".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 103(2021):11.

入库方式： OAI收割

来源：地理科学与资源研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。