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).
Step 1
To get started, clone this repository:
git clone https://github.com/guanyuezhen/UFCD.gitNext, create the conda environment named ufcd by executing the following command:
conda create -n ufcd python=3.8Install necessary packages:
pip install -r requirements.txtStep 2
Prepare the change detection datasets following ./data/README.md.
Step 3
Train/Test:
sh ./scripts/train.sh
sh ./scripts/test.sh 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 |
This repository is built with the help of the projects:
Simple-Remote-Sensing-Change-Detection-Framework
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}
}
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