How can overhead imagery, geotagged media, and geospatial AI support understanding of cities and the built environment? We study land use, property and infrastructure patterns, traffic dynamics, and human-centered landscape perception from satellite, aerial, and social data. Recent work includes few-shot land use classification from geo-tagged CLIP representations, multimodal landscape scenicness assessment, active geospatial search for housing outreach, soundscape mapping in urban settings, and traffic modeling from overhead imagery. These projects connect our geospatial AI and representation learning methods to urban planning, public health, and transportation safety applications.
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