Official Pytorch Code base for "Detecting semantic changes from VHR remote sensing images by integrating semantic correlations and change priors" Paper
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.
The code is stable while using Python 3.9.0, CUDA >=12.1
- Clone this repository:
git clone https://github.com/long123524/CPGNet
cd CPGNetTo install all the dependencies using conda or pip:
PyTorch
OpenCV
tqdm
skimage
timm
...
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.
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.
python train_CPG.py
python pred_SCD.py
python Eval_SCD.py
A pretrained weight of PVT-V2 on the ImageNet dataset is provided: https://drive.google.com/file/d/1uzeVfA4gEQ772vzLntnkqvWePSw84F6y/view?usp=sharing
This code-base uses certain code-blocks and helper functions from HGINet and BiSRNet.
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}
}