Exploring Morphology and Morphological Drivers of Dunefields using Deep Learning and Remote Sensing Approaches



Maike is a Clarendon funded DPhil student applying deep learning and satellite remote sensing approaches to research morphological patterns and their drivers in southern African dunefields. She is part of the Landscape Dynamics cluster as well as the CoHESys-lab (Complex Human-Environmental Systems Simulation Laboratory). Maike holds a BSc in Geography and an MSc in Physical Geography from the University of Tübingen (Germany) with a focus on GIS and remote sensing. She is passionate about developing and applying models to explore spatial processes and patterns in different sub-fields of geography.

Her previous research experience includes a variety of modelling projects ranging from simulating spatio-temporal vegetation succession in China to analysing shear-wave velocities in the Los Angeles basin. Her interest in drylands and desert dunes was fostered during her MSc thesis in cooperation with the Max Planck Institute for Chemistry where she studied dunes in south-eastern Kazakhstan using satellite remote sensing, machine learning, and Optically Stimulated Luminescence dating. Maike has taught various classes in GIS, statistics, and remote sensing in both Tübingen and Oxford and has assisted in teaching undergraduate field work and laboratory methods.

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