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).

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}
}