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Volume 34 Issue 5
Oct 2023
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Article Contents
Qinjun Qiu, Zhong Xie, Die Zhang, Kai Ma, Liufeng Tao, Yongjian Tan, Zhipeng Zhang, Baode Jiang. Knowledge Graph for Identifying Geological Disasters by Integrating Computer Vision with Ontology. Journal of Earth Science, 2023, 34(5): 1418-1432. doi: 10.1007/s12583-022-1641-1
Citation: Qinjun Qiu, Zhong Xie, Die Zhang, Kai Ma, Liufeng Tao, Yongjian Tan, Zhipeng Zhang, Baode Jiang. Knowledge Graph for Identifying Geological Disasters by Integrating Computer Vision with Ontology. Journal of Earth Science, 2023, 34(5): 1418-1432. doi: 10.1007/s12583-022-1641-1

Knowledge Graph for Identifying Geological Disasters by Integrating Computer Vision with Ontology

doi: 10.1007/s12583-022-1641-1
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  • Corresponding author: Liufeng Tao,
  • Received Date: 08 Nov 2021
  • Accepted Date: 24 Feb 2022
  • Issue Publish Date: 30 Oct 2023
  • The occurrence of geological disasters can have a large impact on urban safety. Protecting people's safety is the most important concern when disasters occur. Safety improvement requires a large amount of comprehensive and representative risk analysis and a large collection of information related to geological hazards, including unstructured knowledge and experience. To address the relevant information and support safety risk analysis, a geological hazard knowledge graph is developed automatically based on computer vision and domain-geoscience ontology to identify geological hazards from input images while obeying safety rules and regulations, even when affected by changes. In the implementation of the knowledge graph, we design an ontology schema of geological disasters based on a top-down approach, and by organizing knowledge as a logical semantic expression, it can be shared using ontology technologies and therefore enable semantic interoperability. Computer vision approaches are then used to automatically detect a set of entities and attributes, using the data from input images, and object types and their attributes are identified so that they can be stored in Neo4j for reasoning and searching. Finally, a reasoning model for geological hazard identification was developed using the Neo4j database to create nodes, relationships, and their properties for modeling, and geological hazards in the images can be automatically identified by searching the Neo4j database. An application on geological hazard is presented. The results show the effectiveness of the proposed approach in terms of identifying possible potential hazards in geological hazards and assisting in formulating targeted preventive measures.


  • Conflict of Interest
    The authors declare that they have no conflict of interest.
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