Bridge: Leveraging Vision Foundation Models for Efficient Cross-Domain Remote Sensing Segmentation

Network Overview

🔍️🔍️ NEWS

• [2025/9/21] ✨✨ The README.md has been updated.
• [2025/9/19] ✨✨ The [arxiv] paper will coming soon。

📄📄 TODO

• ❎ submit to arxiv
• ❎ upload training code
• ❎ upload Bridge model weights

Clone Repo

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We add mmsegmentation as our repository submodule .

So, one should clone this repository use the script as follows:

clone repository

git clone --recurse-submodules https://github.com/woldier/Bridge

Tips

If one already cloned the project and forgot --recurse-submodules,

 # cloned the project and forgot clone submodules 🥲🥲
 git clone https://github.com/woldier/Bridge 

 # initialize and update each submodule in the repository 🥰🥰
 git submodule update --init

after that, we link submodule-mmseg/mmseg $\to$ mmseg:

soft link

ln -s submodule-mmseg/mmseg mmseg

1. Creating Virtual Environment

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This repo use python-3.8, for nvcc -v with cuda >= 11.6.

torch 2.1.1, cuda 12.1, mmcv 2.1.0, mmengine 0.9.1

Install script

conda create -n  peft-mmpretrain  python==3.8 -y
conda activate peft-mmpretrain


pip install torch==2.1.2+cu121  torchvision==0.16.2+cu121 -f https://download.pytorch.org/whl/torch_stable.html
# for CN user use follow script
pip install torch==2.1.2+cu121  torchvision==0.16.2+cu121 -f https://mirrors.aliyun.com/pytorch-wheels/cu121/  

pip install mmcv==2.1.0 mmengine==0.9.1 -f https://download.openmmlab.com/mmcv/dist/cu121/torch2.1/index.html

pip install -r submodule-mmseg/requirements/runtime.txt

Installation of the reference document refer:

Torch and torchvision versions relationship.

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

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' and '5_Labels_all_noBoundary.zip' are required.

ISPRS Vaihingen

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.

LoveDA

The data could be downloaded from Google Drive here.

Or it can be downloaded from zenodo, you should run the following command:

loveda download

cd /{your_project_base_path}/Bridge/data/LoveDA

# Download Train.zip
wget https://zenodo.org/record/5706578/files/Train.zip
# Download Val.zip
wget https://zenodo.org/record/5706578/files/Val.zip
# Download Test.zip
wget https://zenodo.org/record/5706578/files/Test.zip

2.2 Data set preprocessing

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

file

Bridge/
├── data/
│   ├── LoveDA/
│   │   ├── Test.zip
│   │   ├── Train.zip
│   │   └── Val.zip
├── ├── Potsdam_RGB_DA/
│   │   ├── 2_Ortho_RGB.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:

details

• 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

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The code in coming soon 🤗🤗