Wenyuan Li (李汶原)

Wenyuan Li (李汶原)

University of Hong Kong

📝 Seletected Publications

AgriFM

AgriFM: A Multi-source Temporal Remote Sensing Foundation Model for Agriculture Mapping

Remote Sensing of Environment, 2026

Wenyuan Li, Shunlin Liang, Keyan Chen, and et al

[Paper] [Github]

AgriFM is a foundational model for agricultural mapping that introduces a synchronized spatio-temporal architecture to seamlessly integrate multi-source satellite time-series, enabling precise, data-efficient mapping through advanced pre-training on global land cover fractions.
AsiaWheat

A CNN-Transformer Hybrid Framework for Mapping Annual Wheat Fractional Cover from 2001-2023 using MODIS Satellite Data over Asia

IEEE Journal of Selected Topics in Signal Processing, 2026

Wenyuan Li, Shunlin Liang, Yongzhe Chen, Han Ma, Jianglei Xu, Zhongxin Chen, Husheng Fang, and Fengjiao Zhang

[Paper]

We propose a novel deep learning mapping method that combines Convolutional Neural Networks (CNN) and Transformer models, termed DeepMapping, to produce the first annual Asian fractional wheat cover product, AsiaWheat, at a 250 m spatial resolution spanning from 2001 to 2023, which includes both winter and spring wheat.
DeepPhysiNet

DeepPhysiNet: Bridging Deep Learning and Atmospheric Physics for Accurate and Continuous Weather Modeling

arXiv, 2024

Wenyuan Li, Zili Liu, Keyan Chen, Hao Chen, Shunlin Liang, Zhengxia Zou, Zhenwei Shi

[PDF] [Github]

We propose a unified framework, namely DeepPhysiNet, which can incorporate atmospheric physics into deep learning methods for accurate and continuous weather modeling
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Geographical Supervision Correction for Remote Sensing Representation Learning

IEEE Transactions on Geoscience and Remote Sensing (TGRS), 2022

Wenyuan Li, Keyan Chen, and Zhenwei Shi

[PDF]

We propose a Geographical supervision Correction method (GeCo) for remote sensing representation learning. Deviated geographical supervision generated by GLC products can be corrected adaptively using the correction matrix during network pre-training and joint optimization process is designed to simultaneously update the correction matrix and network parameters.
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Geographical Knowledge-Driven Representation Learning for Remote Sensing Images

IEEE Transactions on Geoscience and Remote Sensing (TGRS), 2021

Wenyuan Li, Keyan Chen, Hao Chen and Zhenwei Shi

[PDF] [Github]

We propose a Geographical Knowledge-driven Representation learning method for remote sensing images (GeoKR), improving network performance and reduce the demand for annotated data. The global land cover products and geographical location associated with each remote sensing image are regarded as geographical knowledge to provide supervision for representation learning and network pre-training.