Biography
Adnan is a GIS Analyst and Researcher at the James Hutton Institute, Dundee. He holds a PhD in Soil Science (Machine Learning) from Abertay University, where his dissertation applied machine learning and smartphone-based imaging to digital soil mapping. His academic background began in chemical engineering, followed by an MSc in Process Modelling and Simulation, before moving into environmental data science.
At the James Hutton Institute, Adnan works spatial modelling across a national climate-change-impacts research portfolio, building machine learning pipelines for digital soil mapping, habitat classification, and climate projection on high-performance computing infrastructure. His work spans Scotland-wide soil property mapping with uncertainty quantification, fire-weather bias-correction pipelines, alpine habitat classification, and the application of Earth observation foundation models to environmental monitoring.
Adnan has published in journals including Geoderma and Environmental Research Letters, and continues to work at the intersection of geospatial data science, machine learning, and environmental policy, translating complex spatial data into decision-ready insight for research and practice.
Research
Fire-Weather Risk Modelling: Working on bias-correction pipelines for wildfire-weather indices over Scotland, improving the reliability of climate projections to support regional wildfire risk assessment.
Soil Monitoring and Mapping: Exploring national-scale digital soil mapping for Scotland, predicting soil properties at high resolution using machine learning, with a focus on uncertainty quantification and disaggregating existing coarse-resolution soil maps.
Habitat Mapping: Working on classification models for alpine and montane vegetation communities, improving accuracy on rare and hard-to-detect habitat classes to support biodiversity monitoring.
LiDAR and Habitat Resilience: Involved in a team building a resilience index for habitat condition mapping, using high-resolution LiDAR data to derive terrain and canopy metrics relevant to habitat structure and condition, combined with foundation Earth observation models to identify structural patterns linked to habitat resilience.
Past research
- Gagkas, M., Gagkas, Z., Jabloun, M., Khan, A., Gimona, A. & Rivington, M. (2026). Climate Change Impacts on Natural Capital. The James Hutton Institute, Aberdeen. doi:10.5281/zenodo.20344529
- RRF Peat Mapping Update Report, Robb, C., Khan, A., Coull, M., Matthews, K. & Thompson, J., 2026, 28p.
- Research output: Other contribution (including Policy/Stakeholder briefings): RRF Resilience Index Interim Report, Macfarlane, F., Khan, A., Robb, C., Matthews, K. & Thompson, J., 2026
- Bittner, D., Smith, J., Leontidis, G., Campbell, G. A. … Khan, A. et al. (2026). Assessing the impact of nature-based solutions on soil health in sub-Saharan Africa through farmer-centred methods. Environmental Research Letters,21(4).
- Khan, A., Aitkenhead, M., Stark, C. R. & Jorat, M. E. (2023). Optimal sampling using Conditioned Latin Hypercube for digital soil mapping: an approach using Bhattacharyya distance. Geoderma, 439, 116660.
- Khan, A., Rafey, A., Faruqi, M. H. Z., Siddiqui, F. Z., Siddiqui, S. A. & Hassan, S. Z. (2024). Performance evaluation of a ground source heat pump system in India using experimental and modelling approach. Next Energy, 5, 100169.
- Shaikh, M. B. N., Raja, S., Ahmed, M., Zubair, M., Khan, A. & Ali, M. (2019). Rice husk ash reinforced aluminium matrix composites: fabrication, characterization, statistical analysis and artificial neural network modelling. Materials Research Express, 6(5), 056518.
