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Geographic Artificial Intelligence supports smart city land management, where modeling complex inter-parcel relationships and extracting effective features remain key challenges for accurate land use classification. Urban areas exhibit diverse relationships including spatial similarity between adjacent blocks, configurational similarity between non-adjacent blocks, and heterogeneous relationships among functional zones. However, existing research lacks comprehensive frameworks to fully describe these complex interaction systems. We propose a graph neural network framework based on higher-order Markov inference that integrates three types of complex relationships for urban land use identification. The framework utilizes social media check-in data to construct a third-order transition matrix, explicitly modeling population mobility’s chain influence mechanism. It employs hypergraph structures to fuse point-of-interest semantic features with remote sensing visual features, capturing similarities among spatially distant but functionally homogeneous areas. Finally, it integrates multi-source feature embeddings and block adjacency relationships through distance-weighted graph attention networks. Empirical studies using real data demonstrate superior performance compared to traditional machine learning methods. Higher-order activity type inference performs optimally in areas with high population density, monofunctional land use, and heterogeneous destination land use patterns for inter-regional travel. This model provides scientific modeling approaches and analytical tools for urban land use planning and smart city management.

Unified framework for multi-type higher-order relationships: an application in urban land use identification