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📖 Introduction

Urban Segmentation is a streamlined, high-performance framework designed for semantic segmentation of remote sensing imagery.

Leveraging the power of UNICOM—a vision foundation model pre-trained on 400 million images—this project demonstrates extreme data efficiency. We achieve SOTA-level segmentation results using only 4 labeled satellite images for training. This repository serves as both a robust baseline for research and an accessible entry point for practitioners.

📖 项目介绍

Urban Segmentation 是一个专为遥感图像语义分割设计的高效框架。

本项目利用了在 4亿 海量数据上预训练的视觉基础模型 UNICOM，展示了极致的数据样本效率。我们仅需 4张 标注的卫星图像进行微调，即可获得极佳的分割效果。这不仅为科研提供了一个强有力的 Baseline，也为初学者提供了一个极简的实战范例。

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⚡ Key Features

• Foundation Model Power: Built upon UNICOM ViT backbones, inheriting robust feature representations.
• Extreme Few-Shot: Achieve high mIoU with minimal annotated data (4 images).
• Plug-and-Play: Minimalist code structure (~200 lines for training) without complex dependencies.
• Scalable: Supports both single-GPU rapid prototyping and multi-GPU distributed training.

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🎨 Visualization | 效果展示

🛠️ Getting Started | 快速上手

1. Installation

git clone https://github.com/anxiangsir/urban_seg.git
cd urban_seg
pip install -r requirements.txt

2. Data Preparation

Download the dataset (CCF Satellite Imagery) from Baidu Cloud (Code: 3ih2).

Structure your directory as follows:

dataset/
├── origin/       # 5 annotated source images
├── test/         # Unlabeled test images
└── train/        # Generated via preprocessing
    ├── images/
    └── labels/

Run the preprocessing script to generate random crops:

python preprocess.py

3. Model Zoo

Download the pre-trained UNICOM weights from the Official Release:

• FP16-ViT-B-32.pt
• FP16-ViT-B-16.pt (Recommended)
• FP16-ViT-L-14.pt

4. Training

Option A: Rapid Prototyping (Single GPU)

# Minimal implementation (~200 lines)
python train_one_gpu.py

Option B: High-Performance Training (Multi-GPU DDP)

# Distributed Data Parallel
torchrun --nproc_per_node 8 train_multi_gpus.py

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📜 Citation

If you find this project or the UNICOM model useful for your research, please consider citing:

@inproceedings{an2023unicom,
  title={Unicom: Universal and Compact Representation Learning for Image Retrieval},
  author={An, Xiang and Deng, Jiankang and Yang, Kaicheng and Li, Jiawei and Feng, Ziyong and Guo, Jia and Yang, Jing and Liu, Tongliang},
  booktitle={ICLR},
  year={2023}
}

🤝 Community & Support

We welcome all contributions! Please feel free to open an issue or submit a pull request.