A Systematic Review of Remote Sensing, GIS, and Artificial Intelligence for Regional Resilience Analysis: Theoretical Foundations and a Conceptual Model

This paper systematically reviews the theoretical foundations of integrating remote sensing (RS), geographic information systems (GIS), and artificial intelligence (AI) for regional resilience analysis. A systematic literature review was conducted using keyword search in Scopus, followed by qualitative content analysis of theoretical and review articles; no numerical computations were performed. The review identifies three primary roles for geospatial data in resilience assessment: continuous monitoring, change detection, and AI-based predictive modeling. Random Forest and Convolutional Neural Networks are identified as the most effective algorithms, while GIS-based resilience indicators and spatial multicriteria decision analysis enable vulnerability mapping and prioritization. Based on these findings, a four-stage conceptual model is proposed. Key challenges include data quality, computational demands, and model interpretability.

کلیدواژه ها:

Regional resilience ، remote sensing ، GIS ، artificial intelligence ، systematic review ، conceptual model