metadata
license: mit
library_name: pytorch
pipeline_tag: image-to-image
JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust Multi-Teacher Knowledge Distillation Framework
This repository contains the model presented in the paper JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust Multi-Teacher Knowledge Distillation Framework. This model utilizes a multi-teacher knowledge distillation (MTKD) framework for change detection (CD) in remote sensing images.
Dataset
The JL1-CD dataset is publicly available and can be downloaded from:
Usage
Install
To set up the environment, follow the installation instructions provided in the OpenCD repository.
Training
The training process for the MTKD framework consists of three steps. Below, we use the Changer-MiT-b0 model as an example:
Step 1: Train the original model
Step 2: Train teacher models for different CAR partitions (e.g., 3 partitions)
Step 3: Train the student model
Testing
Checkpoints
You can download checkpoint files from:
Citation
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}
}