Article: Urban spatial morphology evolution mechanism and resilience assessment based on multi-source data fusion Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.27 pp.23 - 59 Abstract: 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. Inderscience Publishers - linking academia, business and industry through research

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

Authors: Xuan Zhang; Dongdong Guo; Hongwei Kang

Addresses: Yancheng Kindergarten Teachers College, Yancheng, 224005, China ' Yancheng Kindergarten Teachers College, Yancheng, 224005, China ' Yancheng Kindergarten Teachers College, Yancheng, 224005, China

Abstract: 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.

Keywords: urban morphology evolution; multi-source data fusion; urban resilience assessment; deep learning; complex adaptive systems; spatiotemporal analysis.

DOI: 10.1504/IJICT.2026.152535

International Journal of Information and Communication Technology, 2026 Vol.27 No.27, pp.23 - 59

Received: 19 Aug 2025
Accepted: 11 Nov 2025
Published online: 25 Mar 2026 *

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

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