CPGNet

Official Pytorch Code base for "Detecting semantic changes from VHR remote sensing images by integrating semantic correlations and change priors" Paper

Introduction

We propose a change prior-guided network, namely CPGNet, which employs a multi-branch paradigm that integrates supplemental changed information to accurately identify diverse types of land cover changes in very high-resolution (VHR) remote sensing image.

Using the code:

The code is stable while using Python 3.9.0, CUDA >=12.1

• Clone this repository:

git clone https://github.com/long123524/CPGNet
cd CPGNet

To install all the dependencies using conda or pip:

PyTorch
OpenCV
tqdm
skimage
timm
...

Data Format

Make sure to put the files as the following structure:

inputs
└── <train>
    ├── image1
    |   ├── 001.tif
    │   ├── 002.tif
    │   ├── 003.tif
    │   ├── ...
    |
    └── image2
    |   ├── 001.tif
    |   ├── 002.tif
    |   ├── 003.tif
    |   ├── ...
    └── label1
    |   ├── 001.tif
    |   ├── 002.tif
    |   ├── 003.tif
    |   ├── ...
    └── label2
    |   ├── 001.tif
    |   ├── 002.tif
    |   ├── 003.tif
    |   ├── ...
    

For testing and validation datasets, the same structure as the above.

Datasets

JL-1 dataset: https://www.jl1mall.com/store/ResourceCenter.

SECOND dataset: https://drive.google.com/file/d/1mN8jzCKKK27p3ODGoDgepjiRYGQpB34u/view.

A preprocessed dataset of cropland non-agriculturalization in Xiamen is available at https://drive.google.com/file/d/1beZ8aPzQk-MuSoRbI64upvjMfNAYjP0-/view?usp=sharing.

Training

python train_CPG.py

Test

python pred_SCD.py

Evaluation

python Eval_SCD.py

A pretrained weight

A pretrained weight of PVT-V2 on the ImageNet dataset is provided: https://drive.google.com/file/d/1uzeVfA4gEQ772vzLntnkqvWePSw84F6y/view?usp=sharing

Acknowledgements:

This code-base uses certain code-blocks and helper functions from HGINet and BiSRNet.

Citation:

If you find this work useful or interesting, please consider citing the following references.

@article{long2025d,
  title={Detecting semantic changes from VHR remote sensing images by integrating semantic correlations and change priors},
  author={Long, Jiang and Zeng, Hongwei and Zhao, Hang and Lin, Haihan and Li, Junbin},
  journal={International Journal of Applied Earth Observation and Geoinformation},
  volume={144},
  pages={104916},
  year={2025},
  publisher={Elsevier}
}

@article{long2025b,
  title={BGSNet: A boundary-guided Siamese multitask network for semantic change detection from high-resolution remote sensing images},
  author={Long, Jiang and Liu, Sicong and Li, Mengmeng and Zhao, Hang and Jin, Yanmin},
  journal={ISPRS Journal of Photogrammetry and Remote Sensing},
  volume={225},
  pages={221--237},
  year={2025},
  publisher={Elsevier}
}

@article{long2024,
  title={Semantic change detection using a hierarchical semantic graph interaction network from high-resolution remote sensing images},
  author={Long, Jiang and Li, Mengmeng and Wang, Xiaoqin and Stein, Alfred},
  journal={ISPRS Journal of Photogrammetry and Remote Sensing},
  volume={211},
  pages={318--335},
  year={2024},
  publisher={Elsevier}
}

@article{long2025,
  title={SMGNet:A Semantic Map-Guided Multitask Neural Network for Remote Sensing Image Semantic Change Detection},
  author={Long, Jiang and Liu, Sicong and Li, Mengmeng},
  journal={IEEE GEOSCIENCE AND REMOTE SENSING LETTERS},
  volume={22},
  pages={1--5},
  year={2025},
  publisher={IEEE}
}