Spatio-temporal data fusion techniques for modeling digital twin City-Reference-Cited by-同舟云学术

Spatio-temporal data fusion techniques for modeling digital twin City

Published:2024-05-17 Issue: Volume: Page:1-24
ISSN:1009-5020
Container-title:Geo-spatial Information Science
language:en
Short-container-title:Geo-spatial Information Science

Author:

Li Yuejin¹²^ORCID,Chen Shengpeng³,Hwang Kai¹²^ORCID,Ji Xiaoqiang²⁴^ORCID,Lei Zhen³,Zhu Yi³,Ye Feng³,Liu Mengjun³

Affiliation:

1. School of Data Science, The Chinese University of Hong Kong (Shenzhen), Shenzhen, China

2. Center for Artificial Intelligence and Cloud Computing, Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS), Shenzhen, China

3. Urban Governance Business Group, Wuda Geoinformatics Co. Ltd, Wuhan, China

4. School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), Shenzhen, China

Funder

National Natural Science Foundation of China

Shenzhen Science and Technology Program

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10095020.2024.2350175

Reference61 articles.

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3. Brito, G., T. Mombach, and M. Valente. 2019. “Migrating to GraphQL: A Practical Assessment.” Paper presented at the Proceedings of IEEE 26th International Conference on Software Analysis, Evolution and Reengineering, New York: IEEE.

4. Integration of heterogeneous geospatial data in a federated database

5. Smart City and information technology: A review

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