Taimur Khan

Contact / Address

Taimur Khan
Data Scientist

Department Community Ecology (Biozönoseforschung)
Helmholtz-Zentrum
für Umweltforschung - UFZ
Theodor-Lieser-Str. 4
06120 Halle, Germany

Tel: +49 341 6025 4363
taimur.khan@ufz.de

ORCID logo https://orcid.org/0000-0001-7833-5474

LinkedIn | Github | Gitlab


Taimur Khan

Main Focus

I am a software developer and data scientist working at the intersection of machine learning, computer vision, and remote sensing. My focus is on building scalable pipelines and research software that process drone, aerial, and satellite imagery for biodiversity modeling, forest monitoring, and ecosystem forecasting. I specialize in deep learning object detection, segmentation, and temporal prediction tasks, and I also conduct drone missions to collect site-specific data that integrates into larger, HPC-enabled workflows. My goal is to develop transparent and reproducible tools that make environmental data actionable for researchers, individuals, and institutions.

Kooperationen / Projekte | Co-operations / Projects

Current Projects:

PhenoEmbed

Biodiversity Meets Data

WALDRESILIENZ

Biodiversity Digital Twin (BioDT)

DeepTrees




Working Group

Macroecology and Vegetation Science

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. In-review @ Journal of Open Source Software (JOSS).
    https://doi.org/10.13140/RG.2.2.32837.36329
  • 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.