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Local climate zone (LCZ) has become a new standard classification scheme in urban landscapes and showed great potential in urban climate research. Traditional classifiers and ordinary neural networks only consider the spectral or local spatial features of the pixel, ignoring the effect of nonlocal information on the LCZ classification. The graph convolutional network (GCN) has been used to exploit the relationship between adjacent and global land covers owing to the ability to conduct flexible convolution over graphs. In this work, we integrated a convolutional neural network and two GCNs into an end-to-end hybrid framework and generated LCZs directly from the original images. Local-, regional-, and global-level features were extracted and grouped complementarily to foster better performance. Experiments were conducted in six cities around the world to verify the effectiveness of our method. Results showed that the average classification accuracy of the six cities reached 0.956 and performed better than any other comparable model. Ablation experiments also demonstrated the mutual promotion of the different modules. Finally, the small sample experiment provided a practical reference for the LCZ classification in the absence of samples in future.

Coupling Dual Graph Convolution Network and Residual Network for Local Climate Zone Mapping