Text2Earth: Unlocking Text-driven Remote Sensing Image Generation with a Global-Scale Dataset and a Foundation Model

Chenyang Liu, Keyan Chen, Rui Zhao, Zhengxia Zou, and Zhenwei Shi*✉

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• Official repository of the paper: "Text2Earth: Unlocking Text-driven Remote Sensing Image Generation with a Global-Scale Dataset and a Foundation Model"
• The dataset and model will be publicly available here.

Latest Updates

• 2025-02: The dataset and model will be publicly available.
• 2025-01: The paper is available.

Table of Contents

• Git-10M dataset
• Text2Earth model
• Citation

Git-10M dataset

• The Git-10M dataset is a global-scale remote sensing image-text pair dataset, consisting of 10 million image-text pairs with geographical locations and resolution information.
  
  
  
  
  

Text2Earth model

Building on the Git-10M dataset, we developed Text2Earth, a 1.3 billion parameter generative foundation model. Text2Earth excels in resolution-controllable text2image generation and demonstrates robust generalization and flexibility across multiple tasks.

• Zero-Shot text2image generation: Text2Earth can generate specific image content based on user-free text input, without scene-specific fine-tuning or retraining.
  

  
  
  
  

  On the previous benchmark dataset RSICD, Text2Earth surpasses the previous models with a significant improvement of +26.23 FID and +20.95% Zero-shot OA metric.
  

  
  
  

• Unbounded Remote Sensing Scene Construction: Using our Text2Earth, users can seamlessly and infinitely generate remote sensing images on a canvas, effectively overcoming the fixed-size limitations of traditional generative models. Text2Earth’s resolution controllability is the key to maintaining visual coherence across the generated scene during the expansion process.
  

  
  
  

• Remote Sensing Image Editing: Text2Earth can perform scene modifications based on user-provided text such as replacing or removing geographic features. And it ensures that these modifications are seamlessly integrated with the surrounding areas, maintaining continuity and coherence.
  

  
  
  

• Cross-Modal Image Generation: Text2Earth can be used for Text-Driven Multi-modal Image Generation, including RGB, SAR, NIR, and PAN images.
  

  
  
  

  Text2Earth also exhibits potential in Image-to-Image Translation, containing cross-modal translation and image enhancement, such as PAN to RGB (PAN2RGB), NIR to RGB (NIR2RGB), PAN to NIR (PAN2NIR), super-resolution, and image dehazing.
  

  
  
  

Citation

If you find this paper useful in your research, please consider citing:

@article{liu2025text2earth,
  title={Text2Earth: Unlocking Text-driven Remote Sensing Image Generation with a Global-Scale Dataset and a Foundation Model},
  author={Liu, Chenyang and Chen, Keyan and Zhao, Rui and Zou, Zhengxia and Shi, Zhenwei},
  journal={arXiv preprint arXiv:2501.00895},
  year={2025}
}

License

This repo is distributed under MIT License. The code can be used for academic purposes only.