Taimur Khan

Current Focus

• Deep learning for vegetation and ecosystem analysis
• Multi-sensor remote sensing (UAV, airborne, satellite)
• Neural networks architectures in ecological computer vision
• High-performance and cloud computing

Kooperationen / Projekte | Co-operations / Projects

Current Projects:

PhenoEmbed

DeepTrees

Biodiversity Meets Data

WALDRESILIENZ

Biodiversity Digital Twin (BioDT)

Working Group

Selected Works

torchgbif: FAIR PyTorch DataLoaders and DataSets for GBIF data
IASDT-Workflows: Data workflows for the Invasive Alien Species Digital twin
DeepTrees: Deep-Learning based spatiotemporal tree inventorying and monitoring from public orthoimages.
IASDT-Dataserver: Data server for interfacing data stored in LUMI-O
Halle Treecrowns: Deep-learning based modeled tree counts in the city of Halle (Saale) (w/ web interface)
Metalabel: Semantic labels for tabular data
OPeNDAP Data Catalog: Containerized template for serving grid and sequence datasets with OPeNDAP
Snakemake Cookiecutter: Workflow template for Snakemake

Workshops

Designing Dynamic Data Driven Digital Twins in Ecology @ ECEM23
Processing JSON in R @ Internal

Publications

• Khan, T., Arnold, C., & Grover, H. (2025). DeepTrees: Tree Crown Segmentation and Analysis in Remote Sensing Imagery with PyTorch. Journal of Open Source Software (JOSS). https://doi.org/10.21105/joss.08056
• Trantas, A., Mensio, M., Stasinos, S., Gribincea, S., Khan, T., Podareanu, D., & van der Veen, A. (2025). BioAnalyst: A Foundation Model for Biodiversity (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2507.09080
  
• Khan, T., Krebs, J., Gupta, S. K., Renkel, J., Arnold, C., & Nölke, N. (2025). Validation Challenges
  in Large-Scale Tree Crown Segmentations from Remote Sensing Imagery Using Deep Learning: A Case
  Study in Germany. In Communications in Computer and Information Science (pp. 311–323). Springer
  Nature Switzerland. https://doi.org/10.1007/978-3-032-06136-2_30
• Khan, T. (2025). Forecasting Smog Events Using ConvLSTM: A Spatio-Temporal Approach for Aerosol Index Prediction in South Asia (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2508.13891
• Khan, T., de Koning, K., Endresen, D., Chala, D., & Kusch, E. (2025). TwinEco: A unified framework for dynamic data-driven digital twins in ecology. Ecological Informatics, 91, 103407. https://doi.org/10.1016/j.ecoinf.2025.103407
  
• Taubert, F., Rossi, T., Wohner, C., Venier, S., Martinovič, T., Khan, T., ... & Banitz, T. (2024). Prototype Biodiversity Digital Twin: grassland biodiversity dynamics. Research Ideas and Outcomes, 10, e124168. DOI: https://doi.org/10.3897/rio.10.e124168
• Khan, T., El-Gabbas, A., Golivets, M., Souza, A., Gordillo, J., Kierans, D., & Kühn, I. (2024). Prototype Biodiversity Digital Twin: Invasive Alien Species. Research Ideas and Outcomes, 10, e124579. DOI: https://doi.org/10.3897/rio.10.e124579
• Khan, T., Banitz, T., Golivets, M., Grimm, V., Groeneveld, J., Kühn, I., Taubert, F. (2022). Prototyping a Biodiversity Digital Twin. Helmholtz-UFZ Science Days 2022. DOI: https://doi.org/10.5281/zenodo.8079131
• Morche, D., Baewert, H., Schuchardt, A., Faust, M., Weber, M., & Khan, T. (2019). Fluvial sediment transport in the proglacial Fagge river, Kaunertal, Austria. Geomorphology of Proglacial Systems (pp. 219-229). Springer, Cham. DOI: https://doi.org/10.1007/978-3-319-94184-4_13
• Khan, T. (2017). DC Resistivity: Estimating pore moisture distribution and mapping permafrost content in Kaunertal, Austria. Department of Geosciences. Skidmore College.