UFCD

<div align="center"> <img width=500 src="./assest/logo.png" alt="logo" /> </div>

1. Overview of UFCD

UFCD is a Pytorch-based toolbox for three different change detection tasks, including binary change detection (BCD), semantic change detection (SCD), and building damage assessment (BDA).

<div align="center"> <img src="./assest/UFCD.jpg" alt /> </div>

2. Usage

✈️ Step 1

To get started, clone this repository:

git clone https://github.com/guanyuezhen/UFCD.git

Next, create the conda environment named ufcd by executing the following command:

conda create -n ufcd python=3.8

Install necessary packages:

pip install -r requirements.txt

✈️ Step 2

Prepare the change detection datasets following ./data/README.md.

✈️ Step 3

Train/Test:

sh ./scripts/train.sh  
sh ./scripts/test.sh   

3. Currently Supported Models and Datasets

Supported change detection models:

|Model|Task|Paper|Link| |:----|:----|:----|:----| |TFI-GR|BCD|Remote Sensing Change Detection via Temporal Feature Interaction and Guided Refinement|link| |A2Net|BCD/SCD|Lightweight Remote Sensing Change Detection With Progressive Feature Aggregation and Supervised Attention|link| |AR-CDNet|BCD/BDA|Towards Accurate and Reliable Change Detection of Remote Sensing Images via Knowledge Review and Online Uncertainty Estimation|link| |A2Net|SCD|Lightweight Remote Sensing Change Detection With Progressive Feature Aggregation and Supervised Attention|link| |SCanNet/TED|SCD|Joint Spatio-Temporal Modeling for the Semantic Change Detection in Remote Sensing Images|link| |BiSRNet/SSCDL|SCD|Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images|link| |ChangeOS|BDA|Building Damage Assessment for Rapid Disaster Response with a Deep Object-based Semantic Change Detection Framework: From Natural Disasters to Man-made Disasters|link| |ChangeOS-GRM|BDA|-|-|

Supported binary change detection datasets:

|Model|Task|Link| |:----|:----|:----| |LEVIR/LEVIR+|BCD|link| |SYSU|BCD|link| |S2Looking|BCD|link| |SECOND|SCD|link| |Landsat-SCD|SCD|link| |xView2|BDA|link|

4. Acknowledgment

This repository is built with the help of the projects:

BIT_CD

PytorchDeepLearing

SCanNet

Simple-Remote-Sensing-Change-Detection-Framework

5. Ending

If you feel our work is useful, please remember to Star and consider citing our work. Thanks!~😘.

@article{Li_2023_A2Net,
        author={Li, Zhenglai and Tang, Chang and Liu, Xinwang and Zhang, Wei and Dou, Jie and Wang, Lizhe and Zomaya, Albert Y.},
        journal={IEEE Transactions on Geoscience and Remote Sensing}, 
        title={Lightweight Remote Sensing Change Detection With Progressive Feature Aggregation and Supervised Attention}, 
        year={2023},
        volume={61},
        number={},
        pages={1-12},
        doi={10.1109/TGRS.2023.3241436}
}
@article{li2022cd,
        author={Li, Zhenglai and Tang, Chang and Wang, Lizhe and Zomaya, Albert Y.},
        journal={IEEE Transactions on Geoscience and Remote Sensing}, 
        title={Remote Sensing Change Detection via Temporal Feature Interaction and Guided Refinement}, 
        year={2022},
        volume={60},
        number={},
        pages={1-11},
        doi={10.1109/TGRS.2022.3199502}
}