Lei Xu

Orcid: 0000-0002-6454-2963

Affiliations:
  • Wuhan University, State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan, China


According to our database1, Lei Xu authored at least 24 papers between 2019 and 2026.

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

2026
Geographically weighted regression with convolutional neural networks to integrate attribute similarity and spatial proximity.
Int. J. Geogr. Inf. Sci., July, 2026

Blueberry maturity detection model based on dual-branch frequency-aware feature fusion and frequency-enhanced Haar wavelet downsampling.
Comput. Electron. Agric., 2026

2025
Multi-layer feature fusion and attention-enhanced YOLOv9 for rapid field detection of greenhouse blueberry maturity at long and short distances.
Int. J. Digit. Earth, December, 2025

Modeling inter-city population flows: a Deep Radiation model with multisource geographic features.
Int. J. Digit. Earth, December, 2025

Spatiotemporal patterns of human mobility during the COVID-19 pandemic in China.
Geo spatial Inf. Sci., November, 2025

Spatiotemporal Seamless Estimation of Global Surface Soil Moisture Using Triple Collocation, Machine Learning, and Data Assimilation.
IEEE Trans. Geosci. Remote. Sens., 2025

Spatiotemporal Soil Moisture Prediction Using a Causal-Guided Deep Learning Model.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2025

OFPO & KGFPO: Ontology and knowledge graph for flood process observation.
Environ. Model. Softw., 2025

Accurate sub-seasonal root-zone soil moisture prediction using attention-based autoregressive transfer learning and SMAP data.
Int. J. Appl. Earth Obs. Geoinformation, 2025

2024
Spatiotemporal seamless global surface soil moisture.
Dataset, November, 2024

Flood Inundation Probability Estimation by Integrating Physical and Social Sensing Data: Case Study of 2021 Heavy Rainfall in Henan, China.
Remote. Sens., August, 2024

Incorporating spatial autocorrelation into deformable ConvLSTM for hourly precipitation forecasting.
Comput. Geosci., February, 2024

Geographically Weighted Convolutional Long Short-Term Memory Neural Networks: A Geospatial Deep Learning Model for Monthly NDVI Prediction.
IEEE Trans. Geosci. Remote. Sens., 2024

Monthly NDVI Prediction Using Spatial Autocorrelation and Nonlocal Attention Networks.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2024

Modeling Population Mobility Flows: A Hybrid Approach Integrating a Gravity Model and Machine Learning.
ISPRS Int. J. Geo Inf., 2024

2023
Hybrid Deep Learning and S2S Model for Improved Sub-Seasonal Surface and Root-Zone Soil Moisture Forecasting.
Remote. Sens., July, 2023

Applicability Analysis and Ensemble Application of BERT with TF-IDF, TextRank, MMR, and LDA for Topic Classification Based on Flood-Related VGI.
ISPRS Int. J. Geo Inf., June, 2023

Rice Yield Prediction in Hubei Province Based on Deep Learning and the Effect of Spatial Heterogeneity.
Remote. Sens., March, 2023

Monthly Ocean Primary Productivity Forecasting by Joint Use of Seasonal Climate Prediction and Temporal Memory.
Remote. Sens., March, 2023

Exploring the Relationship between the Eco-Environmental Quality and Urbanization by Utilizing Sentinel and Landsat Data: A Case Study of the Yellow River Basin.
Remote. Sens., February, 2023

Spatiotemporal Dynamics of Remote-Sensed Forel-Ule Index for Inland Waters Across China During the COVID-19 Pandemic.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2023

2022
Detecting Spatially Non-Stationary between Vegetation and Related Factors in the Yellow River Basin from 1986 to 2021 Using Multiscale Geographically Weighted Regression Based on Landsat.
Remote. Sens., December, 2022

2020
Using Multi-Temporal MODIS NDVI Data to Monitor Tea Status and Forecast Yield: A Case Study at Tanuyen, Laichau, Vietnam.
Remote. Sens., 2020

2019
A spatiotemporal deep learning model for sea surface temperature field prediction using time-series satellite data.
Environ. Model. Softw., 2019


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