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About

Dr Hannah Dee is a computer scientist whose research sits at the intersection of computer vision, cognitive science, and mobile robotics. Her work is distinguished by a deep fascination with how visual information—particularly shadows and occlusions—can be harnessed for spatial reasoning and robot localisation. Dee’s pioneering contributions include developing qualitative methods for robot self-localisation using cast shadows, a novel approach that draws inspiration from human perceptual psychology. Her 2009 paper on qualitative robot localisation using shadow information (15 citations) and subsequent work on reasoning about shadows in mobile robot environments (12 citations) have established her as a leading voice in this niche area. She also created the influential Aberystwyth Leaf Evaluation Dataset (2016, 36 citations), released to support the EPSRC-funded project “Dynamic Modelling of Plant Growth with Computer Vision” (grant EP/LO17253/1). This dataset has become a valuable resource for advancing image analysis in plant sciences. Dee’s work on probabilistic self-localisation using occlusions (8 citations) further demonstrates her commitment to bridging human spatial reasoning with autonomous systems. Her research not only advances theoretical understanding but also provides practical tools and datasets that empower the wider computer vision and robotics communities.