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

LinkedIn | Github | Gitlab


Taimur Khan

Main Focus

I focus on creating digital twins of ecological systems by leveraging data science, remote sensing, and high-performance computing (HPC). My work involves building research software and data engineering pipelines to model biodiversity, monitor vegetation, and analyze ecosystem dynamics. With experience in GPU-accelerated computing, I design and implement computational workflows that process high-resolution datasets from satellites, drones, and ground-based sensors efficiently.

A key area of my expertise is using deep learning for tree crown detection, segmentation, and analysis, advancing methods for tree inventorying and habitat assessment. As a licensed drone operator, I integrate drone-based multispectral and hyperspectral imagery into these workflows, enabling precise, site-specific data collection to complement large-scale analyses.

By combining expertise in data science, remote sensing, and ecological modeling, my work supports biodiversity conservation and sustainable environmental management. I aim to bridge the gap between technology and ecology, creating actionable solutions that empower researchers and decision-makers.

Kooperationen / Projekte | Co-operations / Projects

Current Projects:

Biodiversity Meets Data (2025)

Biodiversity Digital Twin (BioDT)

DeepTrees


Working Group

Macroecology and Vegetation Science

Selected Works

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., Koning, de K., Endresen, D., Chala, D., Kusch, E. (In-Review) TwinEco: A Unified Framework for Dynamic Data-Driven Digital Twins in Ecology. Ecological Modeling. Pre-print DOI: https://doi.org/10.1101/2024.07.23.604592
  • 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.