Urban growth and environmental pressures in rapidly transforming cities require innovative governance tools that integrate advanced technologies with institutional assessment. This study develops and applies a strategic integration framework that combines spatial analysis, Convolutional Neural Networks (CNNs)-based land-use classification, SHAP-based feature attribution, and stakeholder interviews to evaluate Yerevan, Armenia, as a case of a mid-income city facing accelerated urbanization. The case selection is justified by Yerevan’s rapid built-up expansion, fragmented green areas, and institutional challenges in aligning urban development with sustainability goals. The CNN model achieved 92.4% accuracy in land-use classification, and projections under a business-as-usual scenario indicate a 12.8% increase in built-up areas and a 6.5% decline in green zones by 2030. SHAP analysis identified land surface temperature and NDVI as the most influential predictors, while governance interviews highlighted gaps in regulatory support and technical capacity. The proposed framework advances the literature by integrating AI-driven geospatial analysis with qualitative governance assessment, providing actionable insights for urban policymakers. Findings underscore the potential of combining machine learning, geospatial technologies, and institutional diagnostics to guide smart city planning in transition economies.
Mkhitaryan et al. (Fri,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: