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ChangeMamba

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[IEEE TGRS 2024] ChangeMamba: Remote Sensing Change Detection Based on Spatio-Temporal State Space Model

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<div align="center">
<h1 align="center">ChangeMamba</h1>

<h3>ChangeMamba: Remote Sensing Change Detection with Spatio-Temporal State Space Model</h3>

[Hongruixuan Chen](https://scholar.google.ch/citations?user=XOk4Cf0AAAAJ&hl=zh-CN&oi=ao)<sup>1 #</sup>, [Jian Song](https://scholar.google.ch/citations?user=CgcMFJsAAAAJ&hl=zh-CN)<sup>1,2 #</sup>, [Chengxi Han](https://chengxihan.github.io/)<sup>3</sup>, [Junshi Xia](https://scholar.google.com/citations?user=n1aKdTkAAAAJ&hl=en)<sup>2</sup>, [Naoto Yokoya](https://scholar.google.co.jp/citations?user=DJ2KOn8AAAAJ&hl=en)<sup>1,2 *</sup>

<sup>1</sup> The University of Tokyo, <sup>2</sup> RIKEN AIP,  <sup>3</sup> Wuhan University.

<sup>#</sup> Equal contribution, <sup>*</sup> Corresponding author

[![TGRS paper](https://img.shields.io/badge/TGRS-paper-00629B.svg)](https://ieeexplore.ieee.org/document/10565926)  [![arXiv paper](https://img.shields.io/badge/arXiv-paper-b31b1b.svg)](https://arxiv.org/pdf/2404.03425.pdf) [![Zenodo Models](https://img.shields.io/badge/Zenodo-Models-green)](https://zenodo.org/records/14037769) ![visitors](https://visitor-badge.laobi.icu/badge?page_id=ChenHongruixuan.MambaCD&left_color=%2363C7E6&right_color=%23CEE75F)

</div>

## 🛎️Updates
* **` Notice🐍🐍`**: ChangeMamba has been accepted by [IEEE TGRS](https://ieeexplore.ieee.org/document/10565926)! We'd appreciate it if you could give this repo a ⭐️**star**⭐️ and stay tuned!!
* **` Mar. 30th, 2026`**: We have reorganized code to streamline the repo!!
* **` Mar. 14th, 2026`**: ChangeMamba has once again been selected as 🔥ESI Hot Paper and Highly Cited Paper🏆!!
* **` May 21st, 2025`**: We have updated the script to train ChangeMamba on [BRIGHT dataset](https://github.com/ChenHongruixuan/BRIGHT) and released [model's checkpoints](https://zenodo.org/records/14037769)!!
* **` Nov. 13th, 2025`**: ChangeMamba has been selected as 🔥ESI Hot Paper and Highly Cited Paper🏆 for 12 consecutive months!!
* **` Nov. 14th, 2024`**: ChangeMamba has been selected as 🔥ESI Hot Paper🔥!!
* **` Sept. 14th, 2024`**: ChangeMamba has been selected as 🏆ESI Highly Cited Paper🏆!!
* **` Aug. 05th, 2024`**: ChangeMamba has been selected as [IEEE TGRS Popular Paper](https://ieeexplore.ieee.org/xpl/topAccessedArticles.jsp?punumber=36)!!
* **` July 19th, 2024`**: ChangeMamba has been selected as [IEEE GRSS Weekly Paper](https://www.linkedin.com/feed/update/urn:li:activity:7219970529498214400/)!!
* **` June 17th, 2024`**: ChangeMamba has been accepted by [IEEE TGRS](https://ieeexplore.ieee.org/document/10565926)!!
* **` June 08th, 2024`**: [Simplified Chinese version](./README_zh-CN.md) of the README file is available!!
* **` April 18th, 2024`**: We have released all weights of ChangeMamba models on BCD tasks. You are welcome [use them](#%EF%B8%8Fresults-taken-away)!!
* **` April 05th, 2024`**: The models and training code for MambaBCD, MambaSCD, and MambaBDA have been organized and uploaded. You are welcome to use them!!

## 🔭Overview

* [**ChangeMamba**](https://ieeexplore.ieee.org/document/10565926) serves as a strong benchmark for change detection tasks, including binary change detection (MambaBCD), semantic change detection (MambaSCD), and building damage assessment (MambaBDA).

* **Spatio-temporal relationship learning methods of ChangeMamba**

## 🗝️Let's Get Started!
### `A. Installation`

Note that the code in this repo runs under **Linux** system. We have not tested whether it works under other OS.

The repo is based on the [VMamba repo](https://github.com/MzeroMiko/VMamba), thus you need to install it first. The following installation sequence is taken from the VMamba repo.

**Step 1: Clone the repository:**

Clone this repository and navigate to the project directory:
```bash
git clone https://github.com/ChenHongruixuan/ChangeMamba.git
cd ChangeMamba
```

**Step 2: Environment Setup:**

It is recommended to set up a conda environment and installing dependencies via pip. Use the following commands to set up your environment:

***Create and activate a new conda environment***

```bash
conda create -n changemamba
conda activate changemamba
```

***Install dependencies***

```bash
pip install -r requirements.txt
cd kernels/selective_scan && pip install .
```

<!-- 
***Dependencies for "Detection" and "Segmentation" (optional in VMamba)***

```bash
pip install mmengine==0.10.1 mmcv==2.1.0 opencv-python-headless ftfy regex
pip install mmdet==3.3.0 mmsegmentation==1.2.2 mmpretrain==1.2.0
``` -->
### `B. Download Pretrained Weight`
Also, please download the pretrained weights of [VMamba-Tiny](https://zenodo.org/records/14037769), [VMamba-Small](https://zenodo.org/records/14037769), and [VMamba-Base](https://zenodo.org/records/14037769) and put them under 
```bash
project_path/ChangeMamba/pretrained_weight/
```

### `C. Data Preparation`
***Binary change detection***

The three datasets [SYSU](https://github.com/liumency/SYSU-CD), [LEVIR-CD+](https://chenhao.in/LEVIR/) and [WHU-CD](http://gpcv.whu.edu.cn/data/building_dataset.html) are used for binary change detection experiments. Please download them and make them have the following folder/file structure:
```
${DATASET_ROOT}   # Dataset root directory, for example: /home/username/data/SYSU
├── train
│   ├── T1
│   │   ├──00001.png
│   │   ├──00002.png
│   │   ├──00003.png
│   │   ...
│   │
│   ├── T2
│   │   ├──00001.png
│   │   ... 
│   │
│   └── GT
│       ├──00001.png 
│       ...   
│   
├── test
│   ├── ...
│   ...
│  
├── train_set.txt   # Data name list, recording all the names of training data
└── test_set.txt    # Data name list, recording all the names of testing data
```

***Semantic change detection***

The [SECOND dataset](https://captain-whu.github.io/SCD/) is used for semantic change detection experiments. Please download it and make it have the following folder/file structure. Note that **the land-cover maps are RGB images in the original SECOND dataset for visualization, you need to transform them into single-channel**. Also, **the binary change maps should be generated by yourself** and put them into folder [`GT_CD`].

Or you are welcome to directly download and use our [preprocessed SECOND dataset](https://zenodo.org/records/14037769).

```
${DATASET_ROOT}   # Dataset root directory, for example: /home/username/data/SECOND
├── train
│   ├── T1
│   │   ├──00001.png
│   │   ├──00002.png
│   │   ├──00003.png
│   │   ...
│   │
│   ├── T2
│   │   ├──00001.png
│   │   ... 
│   │
│   ├── GT_CD   # Binary change map
│   │   ├──00001.png 
│   │   ... 
│   │
│   ├── GT_T1   # Land-cover map of T1
│   │   ├──00001.png 
│   │   ...  
│   │
│   └── GT_T2   # Land-cover map of T2
│       ├──00001.png 
│       ...  
│   
├── test
│   ├── ...
│   ...
│ 
├── train_set.txt
└── test_set.txt
```

***Building damage assessment***

The xBD dataset can be downloaded from [xView 2 Challenge website](https://xview2.org/dataset). After downloading it, please organize it into the following structure: 
```
${DATASET_ROOT}   # Dataset root directory, for example: /home/username/data/xBD
├── train
│   ├── images
│   │   ├──guatemala-volcano_00000000_pre_disaster.png
│   │   ├──guatemala-volcano_00000000_post_disaster.png
│   │   ...
│   │
│   └── masks
│       ├──guatemala-volcano_00000003_pre_disaster_target.png
│       ├──guatemala-volcano_00000003_post_disaster_target.png
│       ... 
│   
├── test
│   ├── ...
│   ...
│
├── holdout
│   ├── ...
│   ...
│
├── train_set.txt    # Data name list, recording all the names of training data
├── test_set.txt     # Data name list, recording all the names of testing data
└── holdout_set.txt  # Data name list, recording all the names of holdout data
```

### `D. Model Training`
Before training models, please enter into [`changedetection`] folder, which contains all the code for network definitions, training and testing.

```bash
cd <project_path>/ChangeMamba/changedetection
```

***Binary change detection***

The following commands show how to train and evaluate MambaBCD-Small on the SYSU dataset:
```bash
python script/train_MambaBCD.py  --dataset 'SYSU' \
                                 --batch_size 16 \
                                 --crop_size 256 \
                                 --max_iters 20000 \
                                 --model_type MambaBCD_Small \
                                 --model_param_path '<project_path>/ChangeMamba/changedetection/saved_models' \
                                 --train_dataset_path '<dataset_path>/SYSU/train' \
                                 --train_data_list_path '<dataset_path>/SYSU/train_set.txt' \
                                 --test_dataset_path '<dataset_path>/SYSU/test' \
                                 --test_data_list_path '<dataset_path>/SYSU/test_set.txt' \
                                 --cfg '<project_path>/ChangeMamba/changedetection/configs/vssm1/vssm_small_224.yaml' \
                                 --encoder_pretrained_path '<project_path>/ChangeMamba/pretrained_weight/vssm_small_0229_ckpt_epoch_222.pth'
```

***Semantic change detection***

The following commands show how to train and evaluate MambaSCD-Small on the SECOND dataset:
```bash
python script/train_MambaSCD.py  --dataset 'SECOND' \
                                 --batch_size 16 \
                                 --crop_size 512 \
                                 --max_iters 50000 \
                                 --model_type MambaSCD_Small \
                                 --model_param_path '<project_path>/ChangeMamba/changedetection/saved_models' \
                                 --train_dataset_path '<dataset_path>/SECOND/train' \
                                 --train_data_list_path '<dataset_path>/SECOND/train_set.txt' \
                                 --test_dataset_path '<dataset_path>/SECOND/test' \
                                 --test_data_list_path '<dataset_path>/SECOND/test_set.txt' \
                                 --cfg '<project_path>/ChangeMamba/changedetection/configs/vssm1/vssm_small_224.yaml' \
                                 --encoder_pretrained_path '<project_path>/ChangeMamba/pretrained_weight/vssm_small_0229_ckpt_epoch_222.pth'
```

***Building Damage Assessment***

The following commands show how to train and evaluate MambaBDA-Small on the xBD dataset:
```bash
python script/train_MambaBDA.py  --dataset 'xBD' \
                                 --batch_size 16 \
                                 --crop_size 512 \
                                 --max_iters 80000 \
                                 --model_type MambaBDA_Small \
                                 --model_param_path '<project_path>/ChangeMamba/changedetection/saved_models' \
                                 --train_dataset_path '<dataset_path>/xBD/train' \
                                 --train_data_list_path '<dataset_path>/xBD/train_set.txt' \
                                 --test_dataset_path '<dataset_path>/xBD/test' \
                                 --test_data_list_path '<dataset_path>/xBD/test_set.txt' \
                                 --cfg '<project_path>/ChangeMamba/changedetection/configs/vssm1/vssm_small_224.yaml' \
                                 --encoder_pretrained_path '<project_path>/ChangeMamba/pretrained_weight/vssm_small_0229_ckpt_epoch_222.pth'
```
### `E. Inference Using Our/Your Weights`

Before inference, please enter into [`changedetection`] folder. 
```bash
cd <project_path>/ChangeMamba/changedetection
```

***Binary change detection***

The following commands show how to infer binary change maps using trained MambaBCD-Tiny on the LEVIR-CD+ dataset:

* **`Parameter guide`**:
  * `--encoder_pretrained_path`: encoder/backbone pretrained weights only.
  * `--model_checkpoint_path`: full ChangeMamba model weights for inference or weight-only initialization.
  * `--resume_training_path`: training resume checkpoint with optimizer/scheduler/iteration state.
* **`Historical Zenodo checkpoints`**: some published task checkpoints contain model weights only. They still work with `--model_checkpoint_path`, but they should not be treated as full training resumes.

```bash
python script/infer_MambaBCD.py  --dataset 'LEVIR-CD+' \
                                 --model_type 'MambaBCD_Tiny' \
                                 --test_dataset_path '<dataset_path>/LEVIR-CD+/test' \
                                 --test_data_list_path '<dataset_path>/LEVIR-CD+/test_set.txt' \
                                 --cfg '<project_path>/ChangeMamba/changedetection/configs/vssm1/vssm_tiny_224_0229flex.yaml' \
                                 --model_checkpoint_path '<saved_model_path>/MambaBCD_Tiny_LEVIRCD+_F1_0.8803.pth'
```

***Semantic change detection***

The following commands show how to infer semantic change maps using trained MambaSCD-Tiny on the SECOND dataset:
```bash
python script/infer_MambaSCD.py  --dataset 'SECOND'  \
                                 --model_type 'MambaSCD_Tiny' \
                                 --test_dataset_path '<dataset_path>/SECOND/test' \
                                 --test_data_list_path '<dataset_path>/SECOND/test_set.txt' \
                                 --cfg '<project_path>/ChangeMamba/changedetection/configs/vssm1/vssm_tiny_224_0229flex.yaml' \
                                 --model_checkpoint_path '<saved_model_path>/[your_trained_model].pth'
```

***Building damage assessment***

The following commands show how to infer building damage assessment results using trained MambaBDA-Tiny on the xBD dataset:
```bash
python script/infer_MambaBDA.py  --dataset 'xBD'  \
                                 --model_type 'MambaBDA_Tiny' \
                                 --test_dataset_path '<dataset_path>/xBD/test' \
                                 --test_data_list_path '<dataset_path>/xBD/test_set.txt' \
                                 --cfg '<project_path>/ChangeMamba/changedetection/configs/vssm1/vssm_tiny_224_0229flex.yaml' \
                                 --model_checkpoint_path '<saved_model_path>/[your_trained_model].pth'
```

## ⚗️Results Taken Away

* *We'd appreciate it if you could give this repo a ⭐️**star**⭐️ and stay tuned.*

* *Please note that the code we uploaded was reorganised and collated. The models below were also trained using the reorganised code and therefore accuracy may not perfectly match the original paper. In most cases, the accuracy is higher than that in the paper.*

* *We also uploaded prediction results. You can download them and use them directly in your paper [[Zenodo](https://zenodo.org/records/14037769)] [[GDrive](https://drive.google.com/drive/folders/1kVKgbElM23c-hSNZ_TTqfxXNXrPgNJ6L?usp=sharing)][[BaiduYun](https://pan.baidu.com/s/1RYXRL0emsKDL_9_v82nIjQ?pwd=df2t)].*

### `A. Pretrained Weight of VMamba (Encoder)`

| Method | ImageNet (ckpt) | 
| :---: | :---: |
| VMamba-Tiny | [[Zenodo](https://zenodo.org/records/14037769)][[GDrive](https://drive.google.com/file/d/160PXughGMNZ1GyByspLFS68sfUdrQE2N/view?usp=drive_link)][[BaiduYun](https://pan.baidu.com/s/1P9KRVy4lW8LaKJ898eQ_0w?pwd=7qxh)] |   
| VMamba-Small | [[Zenodo](https://zenodo.org/records/14037769)][[GDrive](https://drive.google.com/file/d/1dxHtFEgeJ9KL5WiLlvQOZK5jSEEd2Nmz/view?usp=drive_link)][[BaiduYun](https://pan.baidu.com/s/1RRjTA9ONhO43sBLp_a2TSw?pwd=6qk1)]   | 
| VMamba-Base |  [[Zenodo](https://zenodo.org/records/14037769)][[GDrive](https://drive.google.com/file/d/1kUHSBDoFvFG58EmwWurdSVZd8gyKWYfr/view?usp=drive_link)][[BaiduYun](https://pan.baidu.com/s/14_syzqwNnVB8rD3tejEZ4w?pwd=q825)] |

### `B. Binary Change Detection`

| Method | SYSU (ckpt) | LEVIR-CD+ (ckpt) | WHU-CD (ckpt) | 
| :---: | :---: | :---: | :---: |
| MambaBCD-Tiny | [[Zenodo](https://zenodo.org/records/14037769)][[GDrive](https://drive.google.com/file/d/1qoivh0zrZjpPzUOiIxLWZn7kdBQ-MqnY/view?usp=sharing)][[BaiduYun](https://pan.baidu.com/s/160RiqDQKB6rBwn7Fke6xFQ?pwd=wqf9)] |  [[Zenodo](https://zenodo.org/records/14037769)][[GDrive](https://drive.google.com/file/d/1AtiXBBCoofi1e5g4STYUzBgJ1fYN4VhN/view?usp=drive_link)][[BaiduYun](https://pan.baidu.com/s/13dGC_J-wyIfoPwoPJ5Uc6Q?pwd=8ali)]	 | [[Zenodo](https://zenodo.org/records/14037769)][[GDrive](https://drive.google.com/file/d/1ZLKXhGKgnWoyS0X8g3HS45a3X1MP_QE6/view?usp=drive_link)][[BaiduYun](https://pan.baidu.com/s/1DhTedGZdIC80y06tog1xbg?pwd=raf0)] | 
| MambaBCD-Small | [[Zenodo](https://zenodo.org/records/14037769)][[GDrive](https://drive.google.com/file/d/1ZEPF6CvvFynL-yu_wpEYdpHMHl7tahpH/view?usp=drive_link)][[BaiduYun](https://pan.baidu.com/s/1f8iwuKCkElU9rc24_ZzXBw?pwd=46p5)]   | [[Zenodo](https://zenodo.org/records/14037769)][[GDrive](https://drive.google.com/file/d/19jEBLheCwEnQqF23EqNrn1r79D-nZ95y/view?usp=sharing)][[BaiduYun](https://pan.baidu.com/s/1EKWp-tF0EEGgZ-nVlW8S1g?pwd=n3qz)]  | [[Zenodo](https://zenodo.org/records/14037769)][[GDrive](https://drive.google.com/file/d/1ejiBIhSAJF0P65Xn6DpzRpARiIGPLiWw/view?usp=drive_link)][[BaiduYun]](https://pan.baidu.com/s/1tIWyfJa2o9EMwrKg-gKTnw?pwd=vizm) | 
| MambaBCD-Base |  [[Zenodo](https://zenodo.org/records/14037769)][[GDrive](https://drive.google.com/file/d/14WbK9KjOIOWuea3JAgvIfyDvqACExZ0s/view?usp=drive_link)][[BaiduYun](https://pan.baidu.com/s/1xiWWjlhuJWA40cMggevdlA?pwd=4jft)] |[[Zenodo](https://zenodo.org/records/14037769)][[GDrive](https://drive.google.com/file/d/1uQy5tGXW20xFZvF7hIvZvsi7-JU7tg7G/view?usp=drive_link)] [[BaiduYun](https://pan.baidu.com/s/1M_u7HdIEFIEA2d3L1kfu3Q?pwd=rkgp)] | [[Zenodo](https://zenodo.org/records/14037769)][[GDrive](https://drive.google.com/file/d/1K7aSuT3os7LR9rUvoyVNP-x0hWKZocrn/view?usp=drive_link)][[BaiduYun](https://pan.baidu.com/s/1o6Z6ecIJ59K9eB2KqNMD9w?pwd=4mqd)] |

### `C. Semantic Change Detection`
| Method |  SECOND (ckpt) |
| :---: | :---: |
| MambaSCD-Tiny |  [[Zenodo](https://zenodo.org/records/14037769)][[GDrive](https://drive.google.com/file/d/1Q2hMC320vCpp5MQA8SK54iFY7L5JF9qN/view?usp=sharing)][[BaiduYun](https://pan.baidu.com/s/1eHUjKm8Ty0w92BvOoj53Fw?pwd=6hnj)]  |
| MambaSCD-Small | --  | 
| MambaSCD-Base |[[Zenodo](https://zenodo.org/records/14037769)][[GDrive](https://drive.google.com/file/d/12aJ4sL0r02-rB5K6dixtr6FGJ3kNwlFy/view?usp=sharing)][[BaiduYun](https://pan.baidu.com/s/1GxNDC2JAEvPmOiNArLrYmw?pwd=sr3i)]  |

### `D. Building Damage Assessment`
| Method |  xBD (ckpt) |  BRIGHT (ckpt) | 
| :---: | :---: | :---: |
| MambaBDA-Tiny |  -- |   [[Zenodo](https://zenodo.org/records/14037769)] | 
| MambaBDA-Small | -- | -- |
| MambaBDA-Base | -- | -- |

## 🤔Common Issues
Based on peers' questions from issue, here's a quick navigate list of solutions to some common issues.

| Issue | Solution | 
| :---: | :---: | 
| Issues about SECOND dataset | Please refer to Issue [#13](https://github.com/ChenHongruixuan/ChangeMamba/issues/13) / [#22](https://github.com/ChenHongruixuan/ChangeMamba/issues/22) / [#45](https://github.com/ChenHongruixuan/ChangeMamba/issues/45) |
| CUDA out of memory issue | Please lower the batch size of training and evaluation |
| Modify the model structure | Please refer to Issue [#44](https://github.com/ChenHongruixuan/ChangeMamba/issues/44) |
| NameError: name 'selective_scan_cuda_oflex' is not defined | Please refer to Issue [#9](https://github.com/ChenHongruixuan/ChangeMamba/issues/9) |
| Question about the relationship between iteration, epoch & batch size | Please refer to Issue [#32](https://github.com/ChenHongruixuan/ChangeMamba/issues/32) / [#48](https://github.com/ChenHongruixuan/ChangeMamba/issues/48) |
| Inference using trained models has low accuracy | Please use `--model_checkpoint_path` instead of `--encoder_pretrained_path` to load the trained model's weight |

## 📜Reference

If this code or dataset contributes to your research, please kindly consider citing our paper and give this repo ⭐️ :)
```
@article{chen2024changemamba,
  author={Hongruixuan Chen and Jian Song and Chengxi Han and Junshi Xia and Naoto Yokoya},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={ChangeMamba: Remote Sensing Change Detection with Spatiotemporal State Space Model}, 
  year={2024},
  volume={62},
  number={},
  pages={1-20},
  doi={10.1109/TGRS.2024.3417253}
}
```

## 🤝Acknowledgments
This project is based on VMamba ([paper](https://arxiv.org/abs/2401.10166), [code](https://github.com/MzeroMiko/VMamba)), ScanNet ([paper](https://arxiv.org/abs/2212.05245), [code](https://github.com/ggsDing/SCanNet)), BDANet ([paper](https://ieeexplore.ieee.org/document/9442902), [code](https://github.com/ShaneShen/BDANet-Building-Damage-Assessment)). Thanks for their excellent works!!

## 🙋Q & A
***For any questions, please feel free to [contact us.](mailto:Qschrx@gmail.com)***

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