Journal of Geo-information Science ›› 2021, Vol. 23 ›› Issue (1): 16-28.doi: 10.12082/dqxxkx.2021.200632

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Geographic Knowledge Graph for Remote Sensing Big Data

WANG Zhihua1,2(), YANG Xiaomei1,2,*(), ZHOU Chenghu1,2   

  1. 1. State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2020-10-22 Revised:2020-12-09 Online:2021-01-25 Published:2021-03-25
  • Contact: YANG Xiaomei E-mail:zhwang@lreis.ac.cn;yangxm@lreis.ac.cn
  • Supported by:
    National Natural Science Foundation of China(41901354);National Natural Science Foundation of China(41671436);National Natural Science Foundation of China(41890854)

Abstract:

Due to the temporal and spatial heterogeneity of the complex earth's surface, the traditional idea of developing new intelligent interpretation algorithms to solve the remote sensing geoscience cognition based on the features of remote sensing images has hit the bottleneck in terms of accuracy and geographic usage when analyzing remote sensing big data. To overcome the bottleneck, we proposed the Geographic Knowledge Graph (GKG) that based on the geographic knowledge to analyze the remote sensing big data, which is inspired by the recently proposed Knowledge Graph from the geographic perspective. It expands the concept of the geographic knowledge and classifies the geographic knowledge into three levels: Data knowledge, conception knowledge, and regularity knowledge. Then, it represents and connects all geographic knowledge in Graph by nodes and edges and realizes the feedback iteration and update between different levels of the geographic knowledge. This representation enables GKG to perform well at knowledge inquiring, reasoning, calibration, and expanding. How to construct multiscale high-dimension geo-entities and how to connect different levels of the geographic knowledge with heterogeneous features are two key technologies. These functions make GKG promising in refining existing geographic knowledge in the era of remote sensing big data, promoting remote sensing interpretation accuracy and geographic usage, and promoting the development of geoscience.

Key words: remote sensing big data, remote sensing information extraction, remote sensing intelligent interpretation, land use/cover change, geographic knowledge graph, geo-information Tupu, geographic knowledge, knowledge graph