The rapid growth of big geodata has been facilitated by the development of sensing technologies,such as mobile location,Earth observation,and other sensor networks,as well as by the continuous expansion of their application fields[1-3].In essence,big geodata is a large-scale coverage sample set involving time,space,and attribute dimensions for geographical phenomena[2].Large sampling data on surface factors and human behavior observed over a broad spatial and temporal range by ubiquitous sensors and social sensing form the extension of big geodata,which also enables the study of geographic objects,factors,and evolution from multiple spatiotemporal scales,richer dimensions,and diverse perspectives[1].However,big geodata is facing severe challenges in data organization,management,modeling,expres-sion,and analysis and the problems arising from its extensive sources,diverse expressions,multiple observation scales,sampling frequency fluctuations,and accuracy variations.In this context,an in-depth understanding and mastering of the characteristics of different types of big geodata is an important prerequisite for further extending its geoscientific applications[2].

Yong Ge;Ting Ma;Tao Pei;Huixian Weng;Xin Li;Xining Zhang

State Key Laboratory of Resources and Environmental Information System,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China;College of Resources and Environment,University of Academy of Sciences,Beijing 100049,China;National Tibetan Plateau Data Center,Key Laboratory of Tibetan Environmental Changes and Land Surface Processes,Institute of Tibetan Plateau Research,Chinese Academy of Sciences,Beijing 100101,China;Center for Excellence in Tibetan Plateau Earth Sciences,Chinese Academy of Sciences,Beijing 100101,China

科学通报（英文版）

[1]Yong Ge;Ting Ma;Tao Pei;Huixian Weng;Xin Li;Xining Zhang-.Progress of big geodata)[J].科学通报（英文版）,2022(17):1739-1742

geodata,mastering,geoscientific

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