publications

2025

1. Under Review

   

   GlobalGeoTree: A Multi-Granular Vision-Language Dataset for Global Tree Species Classification

   Yang Mu^*, Zhitong Xiong, Yi Wang, and 4 more authors

   2025

   Abs arXiv Bib Code

   We built a global dataset of 6.3M remote sensing samples covering 21,001 tree species with rich taxonomic labels. We developed GeoTreeCLIP, a CLIP-based vision-language foundation model for joint understanding of remote sensing imagery and species descriptions. Our model achieved strong zero-shot and few-shot performance, setting new benchmarks for multimodal tree species classification. All data and code are open-sourced to support the geospatial AI community.

   @article{mu2025globalgeo,
     title = {GlobalGeoTree: A Multi-Granular Vision-Language Dataset for Global Tree Species Classification},
     author = {Mu, Yang and Xiong, Zhitong and Wang, Yi and Shahzad, Muhammad and Essl, Franz and van Kleunen, Mark and Zhu, Xiao Xiang},
     year = {2025},
     url = {https://arxiv.org/abs/2505.12513},
     dataset = {https://huggingface.co/datasets/yann111/GlobalGeoTree}
   }

2. Int. J. Appl. Earth Obs.

   

   National-scale tree species mapping with deep learning reveals forest management insights in Germany

   Yang Mu^*, Jianhua Guo, Muhammad Shahzad, and 1 more author

   International Journal of Applied Earth Observation and Geoinformation, 2025

   Abs DOI Bib HTML Code

   We designed ForestFormer, a dual-branch neural network with spectral-spatial attention. Our model achieved 84% accuracy across 8 dominant tree species using Sentinel-2 time series data. We generated a comprehensive, high-resolution tree species distribution map for Germany and provided forest management insights on species resistance and biodiversity levels. Code and pre-trained models are released to support open research and applications.

   @article{mu2025national,
     title = {National-scale tree species mapping with deep learning reveals forest management insights in Germany},
     author = {Mu, Yang and Guo, Jianhua and Shahzad, Muhammad and Zhu, Xiao Xiang},
     journal = {International Journal of Applied Earth Observation and Geoinformation},
     volume = {139},
     pages = {104522},
     year = {2025},
     publisher = {Elsevier},
     doi = {10.1016/j.jag.2025.104522},
     url = {https://www.sciencedirect.com/science/article/pii/S1569843225001694},
   }

3. AAAI

   

   MPTSNet: Integrating Multiscale Periodic Local Patterns and Global Dependencies for Multivariate Time Series Classification

   Yang Mu^*, Muhammad Shahzad, and Xiao Xiang Zhu

   In Proceedings of the AAAI Conference on Artificial Intelligence, 2025

   Abs Bib HTML Code

   We designed a Multiscale Periodic Time Series Network for multi-periodicity analysis. Our approach combined CNN-based local pattern extraction with attention mechanisms for global dependency modeling. MPTSNet outperformed 21 existing baseline methods in extensive benchmark datasets. We released codes to support reproducible research and open-source applications.

   @inproceedings{mu2025mpts,
     title = {MPTSNet: Integrating Multiscale Periodic Local Patterns and Global Dependencies for Multivariate Time Series Classification},
     author = {Mu, Yang and Shahzad, Muhammad and Zhu, Xiao Xiang},
     booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
     volume = {39},
     year = {2025},
     organization = {AAAI},
     url = {https://ojs.aaai.org/index.php/AAAI/article/view/34155},
   }