👋 About Me

I am currently a Postdoc Research Fellow in the Department of Geography at the University of Hong Kong, working with Prof. Shunlin Liang. I received my Ph.D. degree from the Image Processing Center of Beihang University, supervised by Prof. Zhenwei Shi.

My primary research interests are in applying deep learning methods, remote sensing data, and remote sensing prior knowledge to achieve applications including crop type recognition in situations with inadequate annotations.

💻 Research Interests

• Crop Mapping and Crop Type Classification. Crop mapping products at Asian and global scale and crop type classification method using deep learning approaches.
• Remote Sensing Data Process with Deep Learning and Global Satellite Products. Using global satellite products to provide supervision information for deep learning methods, which enables remote sensing big data to be processed and analyzed with minimal need for manual annotations.
• Deep Learning Methods for Meteorological Data with physical laws. Deep learning methods to fuse and model spatiotemporal meteorological data for weather forecasting, downscaling, and bias correction.

📝 Seletected Publications

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

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.

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.

Semantic Segmentation of Remote Sensing Images With Self-Supervised Multitask Representation Learning

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS), 2021

Wenyuan Li, Hao Chen and Zhenwei Shi

[PDF] [Github]

We propose a self-supervised multitask representation learning method to capture effective visual representations of remote sensing images. We design three different pretext tasks and a triplet Siamese network to learn the high-level and low-level image features at the same time. The network can be trained without any labeled data, and the trained model can be fine-tuned with the annotated segmentation dataset

Generative Adversarial Training for Weakly Supervised Cloud Matting

International Conference on Computer Vision (ICCV), 2019

Zhengxia Zou, Wenyuan Li, Tianyang Shi, Zhenwei Shi and Jieping Ye.

[PDF] [Github]

We re-examine the cloud detection under a totally different point of view, i.e. to formulate it as a mixed energy separation process between foreground and background images, which can be equivalently implemented under an image matting paradigm with a clear physical significance. We further propose a generative adversarial framework where the training of our model neither requires any pixel-wise ground truth reference nor any additional user interactions.

📖 Educations

• 2018.09 - 2023.06, Ph.D. in Pattern Recognition and Intelligent System, Beihang University, China.
• 2017.09 - 2018.06, M.S. in Pattern Recognition and Intelligent System, Beihang University, China.
• 2013.09 - 2017.06, B.S. in Automatics, North China Electric Power University.