publications

2026

1. UFUG

   

   A Satellite-Based Assessment of Alder Pollen Exposure across 51 Cities in Bavaria, Germany

   Zirui Tang, Yang Mu, Monica Gonzalez-Alonso, and 1 more author

   Urban Forestry and Urban Greening, 2026

   Abs DOI Bib HTML

   We developed a satellite-based framework to assess alder pollen exposure across 51 cities in Bavaria, integrating remote sensing land cover with pollen monitoring data. Our analysis reveals spatial patterns of pollen exposure linked to urban vegetation and supports allergy-aware urban greening and public health planning.

   @article{tang2026satellite,
     title = {A Satellite-Based Assessment of Alder Pollen Exposure across 51 Cities in Bavaria, Germany},
     author = {Tang, Zirui and Mu, Yang and Gonzalez-Alonso, Monica and Li, Qingyu},
     journal = {Urban Forestry and Urban Greening},
     pages = {129548},
     year = {2026},
     publisher = {Elsevier},
     doi = {10.1016/j.ufug.2026.129548},
     url = {https://www.sciencedirect.com/science/article/pii/S1618866726002888},
   }

2. ESSD

   

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

   Yang Mu^*, Zhitong Xiong, Yi Wang, and 5 more authors

   Earth System Science Data, 2026

   Abs DOI arXiv Bib HTML 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{mu2026globalgeo,
     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 Kreft, Holger and van Kleunen, Mark and Zhu, Xiao Xiang},
     journal = {Earth System Science Data},
     volume = {18},
     pages = {1379--1403},
     year = {2026},
     publisher = {Copernicus Publications},
     doi = {10.5194/essd-18-1379-2026},
     url = {https://essd.copernicus.org/articles/18/1379/2026/},
     dataset = {https://huggingface.co/datasets/yann111/GlobalGeoTree}
   }

2025

1. 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},
   }

2. 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},
   }