Intelligent Delineation Algorithm of Urban Development Boundary Based on Graph Neural Network

The Urban Growth Boundary (UGB) is essential for controlling urban sprawl and optimizing land use, yet traditional delineation methods struggle with modeling complex spatial relationships and fusing multi-source data. This study proposes an intelligent UGB delineation algorithm based on a Graph Neural Network (GNN). The study area is discretized into uniform spatial units to construct an urban graph, with node features integrating remote sensing imagery, land use types, transportation networks, and population-economic data. A spatially constrained graph convolution structure with an improved attention mechanism is designed to jointly model spatial structures and expansion driving factors, enhanced by multi-scale feature aggregation and spatial consistency constraints. Experimental validation in a 1,250 km² urban area (5,024 nodes, approximately 3.8×10⁴ edges) demonstrates that the proposed model achieves 0.912 accuracy and 0.900 F1-score in UGB recognition—3.4% and 3.8% higher than traditional GCN—with a 7.2% improvement in spatial consistency. The model remains stable across 250–1000 m spatial scales, indicating strong generalization ability and spatial adaptability. This GNN-based UGB delineation method effectively captures urban spatial structure characteristics and expansion patterns, providing a high-precision, data-driven technical pathway for territorial spatial planning and sustainable urban growth management.