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