Urban Change

Urban change research focuses on understanding and quantifying transformations in cities, aiming to improve urban planning and resource management. Current studies leverage advanced computer vision techniques, including deep learning models like convolutional neural networks and self-supervised learning approaches, to analyze large-scale datasets such as street view imagery and satellite data for fine-grained assessment of changes in housing, infrastructure, and overall urban morphology. These analyses provide valuable insights for predicting the impact of urban interventions on pedestrian safety and informing sustainable urban development strategies, demonstrating the practical utility of these methods for policy and planning.