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

@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: [email protected]

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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