Precise identification of spatial unit functional features in the city is a pre-condition for urban planning and policy-making. However, inferring unknown attributes of urban spatial units from data mining of spatial interaction remains a challenge in geographic information science. Although neural-network approaches have been widely applied to this field, urban dynamics, spatial semantics, and their relationship with urban functional features have not been deeply discussed. To this end, we proposed semantic-enhanced graph convolutional neural networks (GCNNs) to facilitate the multi-scale embedding of urban spatial units, based on which the identification of urban land use is achieved by leveraging the characteristics of human mobility extracted from the largest mobile phone datasets to date. Given the heterogeneity of multi-modal spatial data, we introduced the combination of a systematic data-alignment method and a generative feature-fusion method for the robust construction of heterogeneous graphs, providing an adaptive solution to improve GCNNs’ performance in node-classification tasks. Our work explicitly examined the scale effect on GCNN backbones, for the first time. The results prove that large-scale tasks are more sensitive to the directionality of spatial interaction, and small-scale tasks are more sensitive to the adjacency of spatial interaction. Quantitative experiments conducted in Shenzhen demonstrate the superior performance of our proposed framework compared to state-of-the-art methods. The best accuracy is achieved by the inductive GraphSAGE model at the scale of 250 m, exceeding the baseline by 25.4%. Furthermore, we innovatively explained the role of spatial-interaction factors in the identification of urban land use through the deep learning method.

Urban regeneration is an important policy tool for restructuring urban space, but its implications for operational transport-related carbon emissions remain unclear. Using the City Smart Planning System (CitySPS), this study simulates emissions in Shenzhen from 2020 to 2035 under a baseline and five policy scenarios representing temporal modification, land-use type modification and spatial replacement. The analysis uses legally designated regeneration parcels and multi-source spatial data. The accounting boundary covers CitySPS-represented operational transport-related carbon emissions from urban travel and excludes demolition, construction, building operation, embodied emissions, and other life-cycle sources. The baseline emissions rose from 1.840 × 10⁷ t CO₂ in 2020 to 2.269 × 10⁷ t CO₂ in 2035 (approximately 23%). Relative to the same-year baseline, all the policy scenarios produce higher simulated emissions in 2030 (+0.11% to +1.26%) but lower simulated emissions in 2035 (−0.34% to −1.97%). Under the evaluated configurations and the shared CitySPS assumptions, spatial replacement produces the largest simulated reduction in 2035 (−1.97%) despite an increase in 2030 (+0.41%). The results indicate time-dependent and heterogeneous outcomes across the scenario configurations.