The Predictive Power of Spatial Relational Reasoning Models: A Deep Learning Framework for Structured Spatial Intelligence

Abstract

Spatial reasoning is a fundamental aspect of intelligent behavior, particularly in domains such as autonomous navigation, robotics, urban analytics, and geospatial modeling. This study investigates the predictive capabilities of Spatial Relational Reasoning Models (SRRMs), which explicitly encode spatial dependencies and relational structures between objects or regions in an environment. We propose and implement a deep learning-based framework combining graph neural networks (GNNs), convolutional neural networks (CNNs), and transformer-based architectures to evaluate their performance in spatial prediction tasks. Using both synthetic and publicly available datasets—such as the CLEVR and SpaceNet benchmarks—we conduct comprehensive experiments assessing model accuracy in predicting spatial configurations, relational object placements, and future trajectories. The results demonstrate that SRRMs outperform traditional convolutional and sequence-based models, achieving up to 11% higher prediction accuracy and improved generalization in complex, unseen scenarios. Our discussion highlights the strengths and limitations of relational modeling and suggests directions for scalable, explainable, and cross-domain applications of spatial reasoning. These findings contribute to a deeper understanding of structured spatial intelligence and the evolving role of deep learning in capturing real-world spatial phenomena.