Papers › A Billion-scale Foundation Model for Remote Sensing Images
A Billion-scale Foundation Model for Remote Sensing Images
Keumgang Cha, Junghoon Seo, Taekyung Lee
As the potential of foundation models in visual tasks has garnered significant attention, pretraining these models before downstream tasks has become a crucial step. The three key factors in pretraining foundation models are the pretraining method, the size of the pretraining dataset, and the number of model parameters. Recently, research in the remote sensing field has focused primarily on the pretraining method and the size of the dataset, with limited emphasis on the number of model parameters. This paper addresses this gap by examining the effect of increasing the number of model parameters on the performance of foundation models in downstream tasks such as rotated object detection and semantic segmentation. We pretrained foundation models with varying numbers of parameters, including 86M, 605.26M, 1.3B, and 2.4B, to determine whether performance in downstream tasks improved with an increase in parameters. To the best of our knowledge, this is the first billion-scale foundation model in the remote sensing field. Furthermore, we propose an effective method for scaling up and fine-tuning a vision transformer in the remote sensing field. To evaluate general performance in downstream tasks, we employed the DOTA v2.0 and DIOR-R benchmark datasets for rotated object detection, and the Potsdam and LoveDA datasets for semantic segmentation. Experimental results demonstrated that, across all benchmark datasets and downstream tasks, the performance of the foundation models and data efficiency improved as the number of parameters increased. Moreover, our models achieve the state-of-the-art performance on several datasets including DIOR-R, Postdam, and LoveDA.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Object Detection In Aerial Images | DIOR-R | ViT-G12X4 | mAP | 73.60 | #2 of 9 | Archive leaderboard | report |
| Semantic Segmentation | ISPRS Potsdam | ViT-G12X4 | Mean F1 | 92.12 | #2 of 20 | Archive leaderboard | report |
| Semantic Segmentation | ISPRS Potsdam | ViT-G12X4 | Overall Accuracy | 92.58 | #2 of 20 | Archive leaderboard | report |
| Semantic Segmentation | LoveDA | ViT-G12X4 | Category mIoU | 54.4 | #4 of 19 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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