Papers › RemoteCLIP: A Vision Language Foundation Model for Remote Sensing
RemoteCLIP: A Vision Language Foundation Model for Remote Sensing
Fan Liu, Delong Chen, Zhangqingyun Guan, Xiaocong Zhou, Jiale Zhu, Qiaolin Ye, Liyong Fu, Jun Zhou
General-purpose foundation models have led to recent breakthroughs in artificial intelligence. In remote sensing, self-supervised learning (SSL) and Masked Image Modeling (MIM) have been adopted to build foundation models. However, these models primarily learn low-level features and require annotated data for fine-tuning. Moreover, they are inapplicable for retrieval and zero-shot applications due to the lack of language understanding. To address these limitations, we propose RemoteCLIP, the first vision-language foundation model for remote sensing that aims to learn robust visual features with rich semantics and aligned text embeddings for seamless downstream application. To address the scarcity of pre-training data, we leverage data scaling which converts heterogeneous annotations into a unified image-caption data format based on Box-to-Caption (B2C) and Mask-to-Box (M2B) conversion. By further incorporating UAV imagery, we produce a 12 × larger pretraining dataset than the combination of all available datasets. RemoteCLIP can be applied to a variety of downstream tasks, including zero-shot image classification, linear probing, k-NN classification, few-shot classification, image-text retrieval, and object counting in remote sensing images. Evaluation on 16 datasets, including a newly introduced RemoteCount benchmark to test the object counting ability, shows that RemoteCLIP consistently outperforms baseline foundation models across different model scales. Impressively, RemoteCLIP beats the state-of-the-art method by 9.14% mean recall on the RSITMD dataset and 8.92% on the RSICD dataset. For zero-shot classification, our RemoteCLIP outperforms the CLIP baseline by up to 6.39% average accuracy on 12 downstream datasets. Project website: https://github.com/ChenDelong1999/RemoteCLIP
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Code
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Cross-Modal Retrieval | RSICD | RemoteCLIP | Image-to-text R@1 | 18.39% | #4 of 10 | Archive leaderboard | report |
| Cross-Modal Retrieval | RSICD | RemoteCLIP | Mean Recall | 36.35% | #4 of 10 | Archive leaderboard | report |
| Cross-Modal Retrieval | RSICD | RemoteCLIP | text-to-image R@1 | 14.73% | #4 of 10 | Archive leaderboard | report |
| Cross-Modal Retrieval | RSITMD | RemoteCLIP | Image-to-text R@1 | 28.76% | #4 of 10 | Archive leaderboard | report |
| Cross-Modal Retrieval | RSITMD | RemoteCLIP | Mean Recall | 50.52% | #4 of 10 | Archive leaderboard | report |
| Cross-Modal Retrieval | RSITMD | RemoteCLIP | text-to-imageR@1 | 23.76% | #4 of 10 | Archive leaderboard | report |
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Methods
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