Learning Spatiotemporal Manifold Representation for Probabilistic Land Deformation Prediction-Reference-Cited by-同舟云学术

Learning Spatiotemporal Manifold Representation for Probabilistic Land Deformation Prediction

Published:2024-01 Issue:1 Volume:54 Page:572-585
ISSN:2168-2267
Container-title:IEEE Transactions on Cybernetics
language:
Short-container-title:IEEE Trans. Cybern.

Author:

Xu Xovee¹^ORCID,Zhong Ting²^ORCID,Zhou Fan²^ORCID,Li Rongfan²,Trajcevski Goce³^ORCID,Meng Qinggang⁴^ORCID

Affiliation:

1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

2. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China

3. Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, USA

4. Department of Computer Science, Loughborough University, Loughborough, U.K

Funder

National Natural Science Foundation of China

Natural Science Foundation of Sichuan Province, China

National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Human-Computer Interaction,Information Systems,Control and Systems Engineering,Software

Link

https://ieeexplore.ieee.org/ielam/6221036/10367425/10190739-aam.pdf

Reference65 articles.

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3. The challenge to use multi-temporal InSAR for landslide early warning

4. Design and implementation of a landslide early warning system

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