🤖 AI Summary
Addressing the lack of clear prioritization in urban green space development and the insufficient integration of ecological benefits with social equity—particularly in megacities like Tehran—this study proposes the first quantitative framework for assessing functional importance of urban green spaces by fusing heterogeneous multi-source data (e.g., time-series remote sensing imagery, point-of-interest data, and demographic statistics). Methodologically, it innovatively integrates graph neural networks, multi-objective Bayesian optimization, and remote sensing semantic segmentation to formulate a joint optimization model that jointly maximizes ecosystem services (e.g., urban heat island mitigation and biodiversity support) while enforcing social equity constraints. Empirical validation across five megacities demonstrates a 37% improvement in green space spatial allocation efficiency, a 2.4-fold increase in service coverage for low-income communities, and an F1-score of 91.2%. The framework provides a transferable methodological foundation for sustainable, equity-aware urban green infrastructure planning.