Datasets:
File size: 4,510 Bytes
3a0015b 4db1198 3a0015b 4db1198 3a0015b 5db56f1 1d2c629 5db56f1 3a0015b 5db56f1 4db1198 5db56f1 4db1198 5db56f1 1d2c629 5db56f1 3a0015b 1d2c629 4db1198 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 |
---
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**: [email protected]
---
## 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 |