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license: cc-by-nc-nd-4.0 |
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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. |
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## CC-BY-NC-ND-4.0 License: This dataset is not allowed to be modified or distributed without authorization! |
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<h1> |
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<a href="https://chen-yang-liu.github.io/Text2Earth/">Project Page: https://chen-yang-liu.github.io/Text2Earth/ </a> |
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</h1> |
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<div align="center"> |
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<img src="https://github.com/Chen-Yang-Liu/Text2Earth/raw/main/images/dataset.png" width="1000"/> |
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</div> |
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## Load Dataset |
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```python |
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from modelscope.msdatasets import MsDataset |
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ds = MsDataset.load('lcybuaa/Git-10M') |
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``` |
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## View samples from the dataset |
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```python |
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from datasets import load_dataset |
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save_path = 'xxxxx' |
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ds = load_dataset.load('lcybuaa/Git-10M', cache_dir=save_path) |
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train_dataset = ds["train"] |
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for i, example in enumerate(train_dataset): |
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image = example["image"] |
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# Text Description |
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text = example["text"].split('_GOOGLE_LEVEL_)[-1] |
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# Image Resolution |
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Level = int(example["text"].split('_GOOGLE_LEVEL_)[0]) |
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if Level != 0: |
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Resolution = 2**(17-Level) |
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else: |
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print('This image comes from a public dataset. There is no available resolution metadata.') |
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# save image |
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image.save(f"image_{i}.png") # |
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print('text:', text) |
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``` |
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## Git-RSCLIP: Remote Sensing Vision-Language Contrastive Pre-training Foundation Model |
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Git-RSCLIP is pre-trained using the contrastive learning framework on the Git-10M dataset. |
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Git-RSCLIP is here：[[Huggingface](https://huggingface.co/lcybuaa/Git-RSCLIP) | [Modelscope](https://modelscope.cn/models/lcybuaa1111/Git-RSCLIP)] |
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Compare the Top1-Acc of Zero-shot classification on multiple image classification datasets: |
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| Method | OPTIMAL31 | RSC11 | RSICB128 | WHURS19 | RS2800/RSSCN7 | CLRS | Average score | |
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| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | |
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| CLIP | 0.6 | 0.45 | 0.25 | 0.77 | 0.52 | 0.56 | 0.52 | |
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| RemoteCLIP | 0.82 | 0.67 | 0.34 | 0.93 | 0.52 | 0.66 | 0.65 | |
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| GeoRSCLIP | 0.83 | 0.67 | 0.35 | 0.89 | 0.63 | 0.69 | 0.68 | |
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| SkyCLIP50 | 0.77 | 0.60 | 0.38 | 0.78 | 0.55 | 0.61 | 0.62 | |
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| (Git-RSCLIP) Ours | **0.95** | **0.67** | **0.52** | **0.94** | **0.64** | **0.65** | **0.73** | |
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# BibTeX entry and citation info |
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```bibtex |
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@misc{liu2025text2earthunlockingtextdrivenremote, |
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title={Text2Earth: Unlocking Text-driven Remote Sensing Image Generation with a Global-Scale Dataset and a Foundation Model}, |
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author={Chenyang Liu and Keyan Chen and Rui Zhao and Zhengxia Zou and Zhenwei Shi}, |
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year={2025}, |
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eprint={2501.00895}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2501.00895}, |
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} |
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``` |
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