Spatial data classification often faces severe class imbalance and insufficient modeling of spatial dependencies. To address these challenges, we propose Spatially-Aware Generation and Explanation via Large Language Models (SAGE-LLM), a unified framework that integrates LLM-based semantic reasoning with spatial feature construction for imbalanced data. SAGE-LLM performs boundary-aware reasoning on spatially adjacent samples with divergent labels to derive interpretable and discriminative features. It further employs neighborhood-guided aggregation to capture regional context and semantic-driven augmentation to enhance minority-class recognition. Experiments on real-world land-parcel datasets demonstrate that the proposed method consistently improves both F1-score and AUC over strong baselines. These results confirm that integrating spatial reasoning and LLM-based feature generation offers a practical, interpretable, and robust solution for large-scale imbalanced data learning.