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.

城市转型对个人和社区产生了深远的社会影响。本研究通过收集大规模的街景时间序列数据集，提出了一种端到端的变化检测模型，能够有效捕捉城市环境的实际变化，为城市转型的精细评估提供了可能。