README.md

---
language:
- en
- zh
license: mit
task_categories:
- image-segmentation
task_ids:
- semantic-segmentation
tags:
- remote-sensing
- change-detection
- satellite-imagery
- high-resolution
- multi-temporal
dataset_info:
  config_name: jl1-cd
  features:
  - name: image_pair
    sequence:
    - name: image
      dtype: image
      shape:
      - 512
      - 512
      - 3
  - name: change_label
    dtype: image
    shape:
    - 512
    - 512
    - 1
  splits:
  - name: train
    num_bytes: 1024000000
    num_examples: 4000
  - name: test
    num_bytes: 256000000
    num_examples: 1000
  download_size: 1280000000
  dataset_size: 1280000000
---

# Dataset Card for JL1-CD

## Dataset Description (English)

### Overview

**JL1-CD** is a large-scale, sub-meter, all-inclusive open-source dataset for remote sensing image change detection (CD). It contains 5,000 pairs of 512×512 pixel satellite images with a resolution of 0.5 to 0.75 meters, covering various types of surface changes in multiple regions of China. JL1-CD includes not only common human-induced changes (e.g., buildings, roads) but also natural changes (e.g., forests, water bodies, grasslands). The dataset aims to provide a comprehensive benchmark for change detection algorithms.

### Dataset Structure

- **Number of Image Pairs**: 5,000 pairs
- **Image Size**: 512×512 pixels
- **Resolution**: 0.5 to 0.75 meters
- **Change Types**: Human-induced changes (e.g., buildings, roads) and natural changes (e.g., forests, water bodies, grasslands)
- **Dataset Split**: 4,000 pairs for training, 1,000 pairs for testing

### Dataset Features

- **High Resolution**: Provides rich spatial information, facilitating visual interpretation.
- **Comprehensive**: Covers various change types, enhancing the generalization capability of algorithms.
- **Open Source**: The dataset is fully open-source, supporting the research community's use and improvement.

### Usage

The JL1-CD dataset can be used to train and evaluate remote sensing image change detection models. The image pairs in the dataset include images from two time points along with corresponding pixel-level change labels. Users can utilize this dataset to develop, test, and optimize change detection algorithms.

### Benchmark Results

The **Multi-Teacher Knowledge Distillation (MTKD)** framework proposed in the paper achieves new state-of-the-art (SOTA) results on the JL1-CD dataset. Experiments demonstrate that the MTKD framework significantly improves the performance of change detection models with various network architectures and parameter sizes.

### Code
The code can be found at https://github.com/circleLZY/MTKD-CD.

### Citation

If you use the JL1-CD dataset, please cite the following paper:

[JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust Multi-Teacher Knowledge Distillation Framework](https://huggingface.co/papers/2502.13407)

```bibtex
@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}
}
```

### License

The JL1-CD dataset is licensed under the **MIT License**, allowing users to freely use, modify, and distribute the dataset.

### Contact

For any questions or suggestions, please contact:  
- **Ziyuan Liu**: liuziyua22@mails.tsinghua.edu.cn  

---

## 数据集描述 (中文)

### 概述

**JL1-CD** 是一个用于遥感图像变化检测（Change Detection, CD）的大规模、亚米级、全要素开源数据集。该数据集包含 5,000 对 512×512 像素的卫星图像，分辨率为 0.5 至 0.75 米，覆盖了中国多个地区的多种地表变化类型。JL1-CD 不仅包含常见的人为变化（如建筑物、道路），还涵盖了自然变化（如森林、水体、草地等）。该数据集旨在为变化检测算法提供一个全面的基准测试平台。

### 数据集结构

- **图像对数量**：5,000 对
- **图像尺寸**：512×512 像素
- **分辨率**：0.5 至 0.75 米
- **变化类型**：人为变化（如建筑物、道路）和自然变化（如森林、水体、草地）
- **数据集划分**：4,000 对用于训练，1,000 对用于测试

### 数据集特点

- **高分辨率**：提供丰富的空间信息，便于视觉解释。
- **综合性**：涵盖多种变化类型，提升算法的泛化能力