README.md

metadata

license: cc-by-nc-nd-4.0

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.

Project Page: https://chen-yang-liu.github.io/Text2Earth/

Load Dataset

from modelscope.msdatasets import MsDataset
ds =  MsDataset.load('lcybuaa/Git-10M')

View samples from the dataset

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 | Modelscope]

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

CC-BY-NC-ND-4.0 License: This dataset is not allowed to be modified or distributed without authorization!

BibTeX entry and citation info

@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},
}