Official implementation for the paper JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust Multi-Teacher Knowledge Distillation Framework. This code is built upon the OpenCD toolbox.
- 2/23/2024 - JL1-CD dataset has been open-sourced.
The JL1-CD dataset is now publicly available. You can download the dataset from:
To set up the environment, follow the installation instructions provided in the OpenCD repository.
The training process for the MTKD framework consists of three steps. Below, we use the Changer-MiT-b0 model as an example:
Run the following command to train the original model:
python tools/train.py configs/changer/changer_ex_mit-b0_512x512_200k_cgwx.py --work-dir /path/to/save/models/Changer-mit-b0/initialTrain the teacher models for small, medium, and large CAR partitions as follows:
python tools/train.py configs/distill-changer/changer_ex_mit-b0_512x512_200k_cgwx-small.py --work-dir /path/to/save/models/Changer-mit-b0/small
python tools/train.py configs/distill-changer/changer_ex_mit-b0_512x512_200k_cgwx-medium.py --work-dir /path/to/save/models/Changer-mit-b0/medium
python tools/train.py configs/distill-changer/changer_ex_mit-b0_512x512_200k_cgwx-large.py --work-dir /path/to/save/models/Changer-mit-b0/largeIn the above two steps, you will have four model versions for Changer-MiT-b0: the original model and three teacher models (small, medium, and large). At this point, the O-P strategy can already be applied.
Initialize the checkpoint paths in configs/distill-changer/distill-changer_ex_mit-b0_512x512_200k_cgwx.py for the student model and teacher models as follows:
checkpoint_studentcheckpoint_teacher_lcheckpoint_teacher_mcheckpoint_teacher_s
Then, run the following command to train the student model:
python tools/train.py configs/distill-changer/distill-changer_ex_mit-b0_512x512_200k_cgwx.py --work-dir /path/to/save/models/Changer-mit-b0/distillAfter this step, you will have the student model trained within the MTKD framework.
Testing the student model trained with MTKD is simple. Run the following command:
python test.py <config-file> <checkpoint>Testing the O-P strategy is more complex. You can refer to the script located at tools/test_pipline/single-partition-3-test.py for more details.
You can download checkpoint files from:
If you find the JL1-CD dataset or our work useful in your research, please consider citing our paper:
@article{liu2025jl1,
title={JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust Multi-Teacher Knowledge Distillation Framework},
author={Liu, Ziyuan and Zhu, Ruifei and Gao, Long and Zhou, Yuanxiu and Ma, Jingyu and Gu, Yuantao},
journal={arXiv preprint arXiv:2502.13407},
year={2025}
}