---
license: mit
task_categories:
- text-to-image
tags:
  - remote-sensing
  - image-generation
  - global-scale
  - high-resolution
---

The **Git-10M** dataset is a global-scale remote sensing image-text pair dataset, consisting of over **10 million** image-text pairs with geographical locations and resolution information.  This dataset was introduced in the paper [Text2Earth: Unlocking Text-driven Remote Sensing Image Generation with a Global-Scale Dataset and a Foundation Model](https://huggingface.co/papers/2501.00895).

[Project Page: https://chen-yang-liu.github.io/Text2Earth/](https://chen-yang-liu.github.io/Text2Earth/)
[GitHub Repository: https://github.com/Chen-Yang-Liu/Text2Earth](https://github.com/Chen-Yang-Liu/Text2Earth)


<div align="center">
  <img src="https://github.com/Chen-Yang-Liu/Text2Earth/raw/main/images/dataset.png" width="1000"/>
</div>

## Load Dataset
```python
from modelscope.msdatasets import MsDataset
ds =  MsDataset.load('lcybuaa/Git-10M')
```


## View samples from the dataset 
```python
from datasets import load_dataset
save_path = 'xxxxx'
ds = load_dataset.load('lcybuaa/Git-10M', cache_dir=save_path)
train_dataset = ds["train"]

for i, example in enumerate(train_dataset):
    image = example["image"]
    # Text Description
    text = example["text"].split('_GOOGLE_LEVEL_)[-1] 
    # Image Resolution
    Level = int(example["text"].split('_GOOGLE_LEVEL_)[0])
    if Level != 0:
        Resolution = 2**(17-Level)
    else:
        print('This image comes from a public dataset. There is no available resolution metadata.')
    # save image
    image.save(f"image_{i}.png")  #
    print('text:', text)
    
```

## Git-RSCLIP: Remote Sensing Vision-Language Contrastive Pre-training Foundation Model
Git-RSCLIP is pre-trained using the contrastive learning framework on the Git-10M dataset.
Git-RSCLIP is here：[[Huggingface](https://huggingface.co/lcybuaa/Git-RSCLIP) | [Modelscope](https://modelscope.cn/models/lcybuaa1111/Git-RSCLIP)]

Compare the Top1-Acc of Zero-shot classification on multiple image classification datasets:

| Method | OPTIMAL31 | RSC11 | RSICB128 | WHURS19 | RS2800/RSSCN7 | CLRS | Average score |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| CLIP | 0.6 | 0.45 | 0.25 | 0.77 | 0.52 | 0.56 | 0.52 |
| RemoteCLIP | 0.82 | 0.67 | 0.34 | 0.93 | 0.52 | 0.66 | 0.65 |
| GeoRSCLIP | 0.83 | 0.67 | 0.35 | 0.89 | 0.63 | 0.69 | 0.68 |
| SkyCLIP50 | 0.77 | 0.60 | 0.38 | 0.78 | 0.55 | 0.61 | 0.62 |
| (Git-RSCLIP) Ours | **0.95** | **0.67** | **0.52** | **0.94** | **0.64** | **0.65** | **0.73** |


## MIT License: This dataset is licensed under the MIT License.


```bibtex
@misc{liu2025text2earthunlockingtextdrivenremote,
      title={Text2Earth: Unlocking Text-driven Remote Sensing Image Generation with a Global-Scale Dataset and a Foundation Model},
      author={Chenyang Liu and Keyan Chen and Rui Zhao and Zhengxia Zou and Zhenwei Shi},
      year={2025},
      eprint={2501.00895},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2501.00895},
}
```