Fine classification of city-scale buildings from satellite remote sensing imagery is a crucial research area with significant implications for urban planning, infrastructure development, and population distribution analysis. However, the task faces big challenges due to low-resolution overhead images acquired from high altitude space-borne platforms and the long-tail sample distribution of fine-grained urban building categories, leading to severe class imbalance problem. To address these issues, we propose a deep network approach to fine-grained classification of urban buildings using open-access satellite images. A Denoising Diffusion Probabilistic Model (DDPM) based super-resolution method is first introduced to enhance the spatial resolution of satellite images, which benefits from domain-adaptive knowledge distillation. Then, a new fine-grained classification network with Category Information Balancing Module (CIBM) and Contrastive Supervision (CS) technique is proposed to mitigate the problem of class imbalance and improve the classification robustness and accuracy. Experiments on Hong Kong data set with 11 fine building types revealed promising classification results with a mean Top-1 accuracy of 60.45\%, which is on par with street-view image based approaches. Extensive ablation study shows that CIBM and CS improve Top-1 accuracy by 2.6\% and 3.5\% compared to the baseline method, respectively. And both modules can be easily inserted into other classification networks and similar enhancements have been achieved. Our research contributes to the field of urban analysis by providing a practical solution for fine classification of buildings in challenging mega city scenarios solely using open-access satellite images. The proposed method can serve as a valuable tool for urban planners, aiding in the understanding of economic, industrial, and population distribution.

借助开放访问的卫星图像，我们提出了一种深度网络方法，用于解决城市建筑的细粒度分类问题，通过空间分辨率增强和类别信息平衡模块的引入来改进分类鲁棒性和准确性，实验证明该方法在高精度图像的分类上与街景图像方法相当，可为城市规划者提供有价值的工具。

UB-FineNet: 面向开放卫星图像的都市建筑细粒度分类网络