Urban spatial morphology evolution mechanism and resilience assessment based on multi-source data fusion

This study develops a framework for urban spatial morphology evolution and resilience assessment through multi-source data fusion.Focusing on Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) from 2010 to 2023, the research integrates satellite imagery, social sensing data, and socioeconomic statistics using hierarchical deep learning.The methodology achieves 92.4% classification accuracy, a 24.1% improvement over single-source methods.Analysis reveals distinct evolution patterns: radial expansion in BTH (4.2% annually), polycentric coalescence in YRD (5.8%), and corridor-oriented development in PRD (6.1%).Resilience assessment demonstrates YRD's superior performance (0.86 index), capturing COVID-19 impacts with 2%-3% decline followed by rapid recovery.Contributions include integrating complex adaptive system theory with spatiotemporal fusion and developing hierarchical architectures with dynamic resilience models.