UB-FineNet: Urban Building Fine-grained Classification Network for
  Open-access Satellite Images
Zhiyi He Wei Yao Jie Shao Puzuo Wang
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.