Urban Green Infrastructure (UGI) plays a critical role in enhancing urban resilience, mitigating climate impacts, and improving environmental quality. This thesis presents a Deep Learning framework for large-scale UGI mapping using Sentinel-2 satellite imagery and auxiliary building footprint data. A transformer-based Swin UNETR model, enhanced with residual channel attention modules, is developed to perform semantic segmentation of UGI across 75 cities representing five Köppen-Geiger climate zones. Reference labels are generated using NDVI thresholding and refined with global building masks to reduce misclassification in built-up areas. The model achieves strong performance, with an average overall accuracy of 94.35%, precision of 91.72%, recall of 95.15%, F1-score of 93.36%, and IOU of 87.68%. Additional validation using high-resolution manually annotated ground truth in five cities reveals that NDVI threshold-based labels tend to generalise vegetation boundaries, masking segmentation errors in narrow or shaded green features. Visual analyses further highlight variations in performance across climatic contexts, with highest accuracy observed in continental and temperate cities. This study confirms the effectiveness of attention-augmented transformer models for scalable UGI mapping and underscores the importance of integrating contextual spatial data and fine-grained validation for reliable urban vegetation monitoring.