Local Climate Zone Mapping by Coupling Multilevel Features With Prior Knowledge Based on Remote Sensing Images

Local climate zone (LCZ) mapping can explore the variability of the impact of urban form on the thermal environment in different urban contexts, and large-scale LCZ mapping can help us to better understand the spatial and temporal dynamics of the climate in urban areas around the world. Studies have indicated that deep learning-based methods can effectively perform the LCZ classification. However, the accuracy of LCZ classification on large-scale datasets is still unsatisfactory, mainly due to the fact that the traditional convolutional neural networks are not good at mining contextual information, which is crucial for fully understanding remote sensing (RS) scenes. In this article, to solve this problem, we propose an LCZ mapping method based on RS images by coupling multilevel features mined from global and local ranges with prior knowledge, named LCZ-MFKNet. The global and local features are extracted through Swin Transformer and space-maintained ResNet (SM-ResNet) model branches, respectively, and then fused through an improved squeeze-and-excitation (iSE) module. The prior knowledge studied from the theoretical definition and experimental tests is that two typical sets of LCZ categories are easily confounded in multiclass classification but separable in two-class classification. Experiments are conducted on the large publicly available So2Sat LCZ42 dataset, where the proposed LCZ-MFKNet method achieved the highest LCZ mapping accuracy. Moreover, six megacities were selected globally for LCZ mapping, and the results verified the accuracy and the general applicability of the proposed LCZ-MFKNet method in large-scale LCZ mapping.