A multi-view graph neural network for building age prediction

Building age is crucial for inferring building energy consumption and understanding the interactions between human behavior and urban infrastructure. Limited by the challenges of surveys, some machine learning methods have been utilized to predict and fill in missing building age data using building footprint. However, the existing methods lack explicit modeling of spatial effects and semantic relationships between buildings. To alleviate these challenges, we propose a novel multi-view graph neural network called Building Age Prediction Network (BAPN). The features of spatial autocorrelation, spatial heterogeneity and semantic similarity were extracted and integrated using multiple graph convolutional networks. Inspired by the spatial regime model, a heterogeneity-aware graph convolutional network (HGCN) based on spatial grouping is designed to capture the spatial heterogeneity. Systematic experiments on three large-scale building footprint datasets demonstrate that BAPN outperforms existing machine learning and graph learning models, achieving high accuracy ranging from 61% to 80%. Moreover, missing building age data within the Fifth Ring Road of Beijing was filled, validating the feasibility of BAPN. This research offers new insights for filling the intra-city building age gaps and understanding multiple spatial effects essential for sustainable urban planning.