EntSeg: Entropy-Guided Pseudo Label Denoising and Masked Image Consistency for Cross-Domain Remote Sensing Segmentation

Network Overview

🔍️🔍️ NEWS

• [2025/11/24] ✨✨ Init Repo.

1. Creating Virtual Environment

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Install script

pip install torch==1.10.2+cu111 -f https://mirror.sjtu.edu.cn/pytorch-wheels/cu111/?mirror_intel_list
pip install torchvision==0.11.3+cu111 -f https://download.pytorch.org/whl/torch_stable.html 
pip install mmcv-full==1.5.0 -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.10.0/index.html
pip install kornia matplotlib prettytable timm yapf==0.40.1

for CN user:

pip install torch==1.10.2+cu111 -f https://mirror.sjtu.edu.cn/pytorch-wheels/cu111/?mirror_intel_list
pip install torchvision==0.11.3+cu111 -f https://download.pytorch.org/whl/torch_stable.html 
pip install mmcv-full==1.5.0 -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.10.0/index.html
pip install kornia matplotlib prettytable timm yapf==0.40.1

Installation of the reference document refer:

Torch and torchvision versions relationship.

Version relationship of mmcv and torch.

2.Preparation of data sets

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We selected Postsdam, Vaihingen and LoveDA as benchmark datasets and created train, val, test lists for researchers.

2.1 Download of datasets

ISPRS Potsdam

Potsdam download

The Potsdam dataset is for urban semantic segmentation used in the 2D Semantic Labeling Contest - Potsdam.

The dataset can be requested at the challenge homepage. The '2_Ortho_RGB.zip', '3_Ortho_IRRG.zip' and '5_Labels_all_noBoundary.zip' are required.

ISPRS Vaihingen

Vaihingen download

The Vaihingen dataset is for urban semantic segmentation used in the 2D Semantic Labeling Contest - Vaihingen.

The dataset can be requested at the challenge homepage. The 'ISPRS_semantic_labeling_Vaihingen.zip' and 'ISPRS_semantic_labeling_Vaihingen_ground_truth_eroded_COMPLETE.zip' are required.

2.2 Data set preprocessing

Place the downloaded file in the corresponding path The format is as follows:

detals

ProSFDA/
├── data/
├── ├── Potsdam_IRRG_DA/
│   │   ├── 3_Ortho_IRRG.zip
│   │   └── 5_Labels_all_noBoundary.zip
├── ├── Vaihingen_IRRG_DA/
│   │   ├── ISPRS_semantic_labeling_Vaihingen.zip
│   │   └── ISPRS_semantic_labeling_Vaihingen_ground_truth_eroded_COMPLETE.zip

after that we can convert dataset:

dataset convert

• Potsdam

python tools/convert_datasets/potsdam.py data/Potsdam_IRRG/ --clip_size 512 --stride_size 512
python tools/convert_datasets/potsdam.py data/Potsdam_RGB/ --clip_size 512 --stride_size 512

• Vaihingen

python tools/convert_datasets/vaihingen.py data/Vaihingen_IRRG/ --clip_size 512 --stride_size 256

Acknowledgements

This project is built upon OpenMMLab. We thank the OpenMMLab developers.

Citation

If you use Geoad in your research, please cite:

@article{,
  title={},
  author={},
  journal={},
  year={}
}