Building pattern recognition by using an edge-attention multi-head graph convolutional network

Effective building pattern recognition, a complex task that requires the simultaneous consideration of individual building features and spatial relations, is essential for successfully generalizing maps. However, existing deep learning approaches must still be adequately comprehensive in jointly quantifying the individual features and spatial relationships of buildings, suggesting further improvement in the quantitative representation of building spaces. This study presents a novel edge-attention multi-head graph convolutional network (GCN) that concurrently considers the quantitative modeling and representation of individual features and spatial relations, enhancing building pattern recognition. The proposed method captures individual building features and spatial relations, including proximity and arrangement similarity, by using spatial relationship descriptors and attention mechanisms to generate spatial relevance coefficients. These coefficients are then integrated into a weighted multi-head GCN to participate in the quantitative expression of individual features, facilitating the quantitative analysis and modeling of building features, and thus, improving recognition performance. Our experimental analysis confirms the method's superior capability in recognizing complex spatial features. The method also demonstrates strong generalization across different scales and areas, underscoring its efficacy and potential for enhancing geospatial analyses.