A Study of AI-Driven Knowledge Distillation for Efficient Geospatial Modeling

Classical spatial econometric models excel in interpreting spatial autocorrelation but struggle with scalability on resource-limited platforms. In contrast, deep neural networks offer high predictive accuracy yet demand significant computational resources. To bridge this divide, we propose a unified knowledge distillation (KD) framework that integrates four established spatial econometric models – Spatial Durbin Error Model (SDEM), Spatial Lag Model (lagsarlm), Spatial Durbin Model (SDM), and Spatial Error Model (SEM) – as teacher models to distill spatial knowledge into lightweight neural student models. Enhanced by AI-driven optimization techniques, our approach reduced mean squared error (MSE) by 20% compared to baseline neural students without distillation in geospatial regression tasks and improved the area under the curve (AUC) by 0.05 in classification tasks. Moreover, the distilled models required approximately 93% fewer floating-point operations (FLOPs) and 50% less memory, enabling real-time applications such as disaster response and urban planning. An open-source Python toolkit accompanies this work to foster further advancements in efficient geospatial intelligence.