# UB-FineNet: Urban Building Fine-grained Classification Network for Open-access Satellite Images

arXiv: https://arxiv.org/abs/2403.02132 · HTML: https://mlsanity.com/paper/2403.02132/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 52 references that are in the map.
- First posted on arXiv 4 Mar 2024.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [Image Transformer](https://mlsanity.com/paper/1802.05751/) (2018)
- [WaveGrad: Estimating Gradients for Waveform Generation](https://mlsanity.com/paper/2009.00713/) (2020)
- [A Neural Representation of Sketch Drawings](https://mlsanity.com/paper/1704.03477/) (2017)
- [EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis](https://mlsanity.com/paper/1612.07919/) (2016)
- [Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models](https://mlsanity.com/paper/1807.05713/) (2018)
- [Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling](https://mlsanity.com/paper/1812.01608/) (2018)
- [Continental-Scale Building Detection from High Resolution Satellite Imagery](https://mlsanity.com/paper/2107.12283/) (2021)
- [Building Instance Classification Using Street View Images](https://mlsanity.com/paper/1802.09026/) (2018)
- [Instance segmentation of fallen trees in aerial color infrared imagery using active multi-contour evolution with fully convolutional network-based intensity priors](https://mlsanity.com/paper/2105.01998/) (2021)

## Built on by (most cited first, 2 of 2)

- [OpenFACADES: An Open Framework for Architectural Caption and Attribute Data Enrichment via Street View Imagery](https://mlsanity.com/paper/2504.02866/) (2025)
- [AI-enabled Satellite Edge Computing: A Single-Pixel Feature based Shallow Classification Model for Hyperspectral Imaging](https://mlsanity.com/paper/2601.18560/) (2026)

## Abstract (arXiv)

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.