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Revisiting spatial optimization in the era of geospatial big data and GeoAI

文献类型：期刊论文

作者Cao, Kai4,5; Zhou, Chenghu3; Church, Richard2; Li, Xia4,5; Li, Wenwen1
刊名INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
出版日期2024-05-01
卷号129页码:103832
关键词Spatial optimization Geospatial big data GeoAI GIS
DOI10.1016/j.jag.2024.103832
产权排序3
文献子类Article
英文摘要Spatial optimization is an interdisciplinary field dedicated to the scientific and rational allocation of resources spatially, which has received tremendous attention across various disciplines including geography, operations research, management science, and computer science. Spatial optimization provides important theoretical foundations and solutions for determining optimal spatial arrangements or configurations of entities, resources, or goods. However, the complexity of spatial optimization problems poses critical challenges in spatial optimization problems modeling, and efficiently solving. Recently, the surge of multi -source geospatial big data, the emerging technologies such as geospatial artificial intelligence (GeoAI), and the advancements of computing technologies along with the ever-expanding capabilities of computer and data storage resources, have created significant opportunities to the effective and efficient addressing of spatial optimization issues, even though numerous challenges still exist. Therefore, this paper aims to revisit the existing literature of spatial optimization quantitatively and qualitatively, as well as reflect on the opportunities and challenges, especially posed by geospatial big data and GeoAI. Through these efforts, we seek to stimulate greater engagement in spatial optimization research and practices, accelerate the integration of novel technologies and methods, as well as collectively advance the development of the field.
WOS关键词DYNAMIC MULTIOBJECTIVE OPTIMIZATION ; HEURISTIC APPROACH ; GENETIC ALGORITHM ; RESERVE SELECTION ; EMERGING TRENDS ; LOCATION ; GIS ; CYBERINFRASTRUCTURE ; OPPORTUNITIES ; INFORMATION
WOS研究方向Remote Sensing
WOS记录号WOS:001236214300001
出版者ELSEVIER
源URL[http://ir.igsnrr.ac.cn/handle/311030/205397]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Cao, Kai; Zhou, Chenghu
作者单位1.Arizona State Univ, Sch Geog Sci & Urban Planning, Tempe, AZ USA
2.Univ Calif Santa Barbara, Dept Geog, Santa Barbara, CA USA
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
4.East China Normal Univ, Key Lab Geog Informat Sci, Minist Educ, Shanghai, Peoples R China
5.East China Normal Univ, Sch Geog Sci, Shanghai, Peoples R China
推荐引用方式
GB/T 7714		 Cao, Kai,Zhou, Chenghu,Church, Richard,et al. Revisiting spatial optimization in the era of geospatial big data and GeoAI[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2024,129:103832.
APA Cao, Kai,Zhou, Chenghu,Church, Richard,Li, Xia,&Li, Wenwen.(2024).Revisiting spatial optimization in the era of geospatial big data and GeoAI.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,129,103832.
MLA Cao, Kai,et al."Revisiting spatial optimization in the era of geospatial big data and GeoAI".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 129(2024):103832.

入库方式： OAI收割

来源：地理科学与资源研究所

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