Home

Awesome

<div align="center"> <h1><a href="https://ieeexplore.ieee.org/document/10591792">Change-Agent: Toward Interactive Comprehensive Remote Sensing Change Interpretation and Analysis</a></h1>

Chenyang Liu, Keyan Chen, Haotian Zhang, Zipeng Qi, Zhengxia Zou, and Zhenwei Shi*✉

<div align="center"> <img src="resource/Change_Agent.png" width="400"/> </div> </div>

Share us a :star: if you're interested in this repo

Official PyTorch implementation of the paper: "Change-Agent: Toward Interactive Comprehensive Remote Sensing Change Interpretation and Analysis" in [IEEE] (Accepted by IEEE TGRS 2024)

News

Table of Contents

LEVIR-MCI dataset

Training of the multi-level change interpretation model

The overview of the MCI model: <br> <div align="center"> <img src="resource/MCI_model.png" width="800"/> </div> <br>

Preparation

Train

Make sure you performed the data preparation above. Then, start training as follows:

python train.py --train_goal 2 --data_folder /DATA_PATH_ROOT/Levir-MCI-dataset/images --savepath ./models_ckpt/

Evaluate

python test.py --data_folder /DATA_PATH_ROOT/Levir-MCI-dataset/images --checkpoint {checkpoint_PATH}

We recommend training the model 5 times to get an average score.

Inference

Run inference to get started as follows:

python predict.py --imgA_path {imgA_path} --imgB_path {imgA_path} --mask_save_path ./CDmask.png

You can modify --checkpoint of Change_Perception.define_args() in predict.py. Then you can use your own model, of course, you also can download our pretrained model MCI_model.pth here: [Hugging face]. After that, put it in ./models_ckpt/.

Construction of Change-Agent

<br> <div align="center"> <img src="resource/overview_agent.png" width="800"/> </div>

Citation

If you find this paper useful in your research, please consider citing:

@ARTICLE{Liu_Change_Agent,
  author={Liu, Chenyang and Chen, Keyan and Zhang, Haotian and Qi, Zipeng and Zou, Zhengxia and Shi, Zhenwei},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={Change-Agent: Toward Interactive Comprehensive Remote Sensing Change Interpretation and Analysis}, 
  year={2024},
  volume={},
  number={},
  pages={1-1},
  keywords={Remote sensing;Feature extraction;Semantics;Transformers;Roads;Earth;Task analysis;Interactive Change-Agent;change captioning;change detection;multi-task learning;large language model},
  doi={10.1109/TGRS.2024.3425815}}

Acknowledgement

Thanks to the following repository:

RSICCformer; Chg2Cap; lagent

License

This repo is distributed under MIT License. The code can be used for academic purposes only.

Contact Us

If you have any other questions❓, please contact us in time 👬