Researching how AI understands the spatial world
My research explores how AI, geospatial data, and Earth observation can be combined to better understand complex spatial and environmental systems; and how those methods can move beyond experiments into practical geospatial workflows. I also collaborate with universities across Canada on applied research projects spanning GeoAI, environmental monitoring, spatial analytics, and emerging AI methods.
View Photos from my Arctic Expeditions
Research Directions
- Geospatial AI Systems: I study and develop ways of integrating modern AI methods into geospatial workflows. This includes geospatial foundation models, computer vision, deep learning for imagery, LLM-powered systems, retrieval-augmented generation, and agentic AI. A central question is how these technologies move from promising models into reliable systems that GIS professionals, researchers, and organizations can actually use.
- Earth Observation & Environmental AI: A second strand of my research focuses on extracting useful information from satellite, aerial, and other spatial observations. My work has included land-cover analysis, carbon monitoring, change detection, remote sensing time series, environmental modelling, and machine learning approaches for interpreting large Earth observation datasets. This work connects advances in GeoAI with practical environmental monitoring and decision support.
- Ocean & Climate Systems: My foundational research focused on Arctic marine carbon cycling and the role of Arctic waters in the global carbon system. Using field observations, satellite data, spatial analysis, and environmental modelling, I studied air-sea CO₂ exchange and variability of carbon fluxes across the Canadian Arctic Archipelago and Hudson Bay. That scientific background continues to shape my approach to AI: complex environmental systems rarely reduce to a single model, dataset, or metric.
Featured Publications
- Ahmed, M., Prikaziuk, E., Laub, M., Klaasse, A.L., Ellerbroek, L.E. (2025). A novel approach to mapping and monitoring land carbon sinks by combining remote sensing and biogeochemical modeling: a case study in Burkina Faso. Ecological Informatics, 90, 103174.
- Ahmed, M., Else, B.G.T., Butterworth, B., Capelle, D.W., Gueguen, C., Miller, L.A., Meilleur, C., Papakyriakou, T. (2021). Widespread surface water pCO2 undersaturation during ice-melt season in an Arctic continental shelf sea (Hudson Bay, Canada). Elementa: Science of the Anthropocene, 9(1), 00130.
- Ahmed, M., Else, B. (2019). The Ocean CO2 sink in the Canadian Arctic Archipelago: a present day budget and past trends due to climate change. Geophysical Research Letters, 46, 9777-9785.
- Barclay, K.M., Gurney-Smith, H.J., Ahmed, M., et al. (2026). Ocean acidification in Canada: the current state of knowledge and pathways for action. Frontiers in Marine Science, 13, 1761703.
- Duke, P.J., Hamme, R.C., Ianson, D., Landschutzer, P., Ahmed, M., Swart, N.C., Covert, P.A. (2023). Estimating marine carbon uptake in the northeast Pacific using a neural network approach. Biogeosciences, 20(18), 3919-3941.