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.

翻译：摘要：城市变革对个体及整个社会具有深远的社会影响。准确评估这些变化对于理解其根本原因并确保可持续城市规划至关重要。传统测量方法在时空粒度上常受限制，难以捕捉实时的物理变化。而街景图像从行人视角捕捉城市空间脉动，可作为一种高分辨率、时效性强且实地可视化的城市变化代理。我们构建了迄今为止规模最大的街景时间序列数据集，并提出一种端到端的变化检测模型，以有效捕捉建成环境中大规模物理变化。通过与既往文献的基准比较及在城市级范围的实施，我们验证了所提方法的有效性。该方法有潜力补充现有数据集，并作为城市变化的细粒度精准评估工具。