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Wanzeng Liu

As seen in: MDPI, Taylor & Francis Online
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Articles

SAFE: sensitive annotation finding and extraction from multi-type Chinese maps via hybrid intelligence and knowledge graph

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By Jiaxin Ren, Wanzeng Liu, Jun Chen
| Taylor & Francis Online
?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom. ABSTRACT Sensitive annotations typically contain key geographic elements or sensitive information vital for geographic information security.
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HI-CMAIM: Hybrid Intelligence-Based Multi-Source Unstructured Chinese Map Annotation Interpretation Model

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By Jiaxin Ren, Wanzeng Liu, Jun Chen, Xiuli Zhu
| MDPI
1. Introduction Maps serve as indispensable tools for understanding and documenting geographic spaces, playing a critical role across social, economic, and scientific domains [1]. Whether used for precise positioning in modern navigation systems, spatial layout optimization in urban planning, or emergency responses in disaster management, maps act as key carriers of spatial information, providing extensive geographic data support [2].
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A Self-Supervised Learning Approach for Extracting China Physical Urban Boundaries Based on Multi-Source Data

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By Yuan Tao, Wanzeng Liu, Jun Chen, Jingxiang Gao
| MDPI
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
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