| ## Requirements | |
| ### Environment | |
| - Python 3.8+ | |
| - PyTorch 2.0.1+ | |
| - CUDA 11.8+ | |
| - Ubuntu 22.04 or higher / Windows 10 | |
| ### Installation | |
| ```bash | |
| conda create --name rscd python=3.8 | |
| conda activate rscd | |
| conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.8 -c pytorch -c nvidia | |
| pip install pytorch-lightning==2.0.5 | |
| pip install scikit-image==0.19.3 numpy==1.24.4 | |
| pip install torchmetrics==1.0.1 | |
| pip install -U catalyst==20.09 | |
| pip install albumentations==1.3.1 | |
| pip install einops==0.6.1 | |
| pip install timm==0.6.7 | |
| pip install addict==2.4.0 | |
| pip install soundfile==0.12.1 | |
| pip install ttach==0.0.3 | |
| pip install prettytable==3.8.0 | |
| pip install -U openmim | |
| pip install triton==2.0.0 | |
| mim install mmcv | |
| pip install -U fvcore | |
| ## Dataset Preparation | |
| We evaluate our method on three public datasets: **LEVIR-CD**, **WHU-CD**, and **CLCD**.[Download](https://drive.google.com/drive/folders/1zxhJ7v3UPgNsKkdvkYCOW7DdKDAAy_ll) | | |