Sheng Yao

Orcid: 0000-0003-0183-6064

Affiliations:
  • China University of Geosciences, Beijing, China


According to our database1, Sheng Yao authored at least 11 papers between 2021 and 2025.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2025
SASTGCN: Semantic-Augmented Spatio-temporal graph convolutional network for subway flow prediction.
Int. J. Appl. Earth Obs. Geoinformation, 2025

2024
Spatiotemporal Characteristics and Prediction of Ecological Safety in the Yellow River Basin of China.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2024

A TDFC-RNNs framework integrated temporal convolutional attention mechanism for InSAR surface deformation prediction: A case study in Beijing Plain.
Int. J. Appl. Earth Obs. Geoinformation, 2024

2023
Analysis of the future trends of typical mountain glacier movements along the Sichuan-Tibet Railway based on ConvGRU network.
Int. J. Digit. Earth, December, 2023

A landslide extraction method of channel attention mechanism U-Net network based on Sentinel-2A remote sensing images.
Int. J. Digit. Earth, December, 2023

A ConvLSTM Neural Network Model for Spatiotemporal Prediction of Mining Area Surface Deformation Based on SBAS-InSAR Monitoring Data.
IEEE Trans. Geosci. Remote. Sens., 2023

2022
A Deep Convolutional Neural Network With Multiscale Feature Dynamic Fusion for InSAR Phase Filtering.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2022

Time-Series Analysis and Prediction of Surface Deformation in the Jinchuan Mining Area, Gansu Province, by Using InSAR and CNN-PhLSTM Network.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2022

An InSAR Interferogram Filtering Method Based on Multi-Level Feature Fusion CNN.
Sensors, 2022

2021
An Extraction Method for Glacial Lakes Based on Landsat-8 Imagery Using an Improved U-Net Network.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2021

A unified network of information considering superimposed landslide factors sequence and pixel spatial neighbourhood for landslide susceptibility mapping.
Int. J. Appl. Earth Obs. Geoinformation, 2021


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