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Liangxiu Han

As seen in: Nature, Frontiers, MDPI, New Food Magazine
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Articles

[2506.20388] A Novel Large Vision Foundation Model (LVFM)-based Approach for Generating High-Resolution Canopy Height Maps in Plantations for Precision Forestry Management

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By Xin Zhang, Liangxiu Han, Huaguo Huang, Han Wang
| arxiv.org
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
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Graph attention automatic encoder based on contrastive learning for domain recognition of spatial transcriptomics - Communications Biology

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By Weichao Ding, Liangxiu Han, Huitong Zhu
| Nature Verified
Abstract Spatial transcriptomics is an emerging technology that enables the profiling of gene expression in tissues while preserving spatial location information. This innovative approach is anticipated to provide a comprehensive understanding of the spatial distribution of different cells within tissues and facilitate in-depth analysis of tissue structure.
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A Generic Self-Supervised Learning (SSL) Framework for Representation Learning from Spectral-Spatial Features of Unlabeled Remote Sensing Imagery

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By Xin Zhang, Liangxiu Han
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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