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Urban transformations have profound societal impact on both individuals andcommunities at large. Accurately assessing these shifts is essential forunderstanding their underlying causes and ensuring sustainable urban planning.Traditional measurements often encounter constraints in spatial and temporalgranularity, failing to capture real-time physical changes. While street viewimagery, capturing the heartbeat of urban spaces from a pedestrian point ofview, can add as a high-definition, up-to-date, and on-the-ground visual proxyof urban change. We curate the largest street view time series dataset to date,and propose an end-to-end change detection model to effectively capturephysical alterations in the built environment at scale. We demonstrate theeffectiveness of our proposed method by benchmark comparisons with previousliterature and implementing it at the city-wide level. Our approach has thepotential to supplement existing dataset and serve as a fine-grained andaccurate assessment of urban change.

CityPulse: Fine-Grained Assessment of Urban Change with Street View Time Series