Muhammad Sohail Danish, Muhammad Akhtar Munir, Syed Roshaan Ali Shah, Muhammad Haris Khan, Rao Muhammad Anwer , Jorma Laaksonen, Fahad Shahbaz Khan, and Salman Khan
Mohamed bin Zayed University of AI, University College London, Aalto University, Linköping University, Australian National University
- Jun-09-25: 🚀 Initial release of TerraFM codebase and pretrained models
- Jun-09-25: 📄 Paper released on arXiv: arxiv link. 🔥🔥
TerraFM is a scalable foundation model designed for unified processing of multisensor Earth Observation (EO) data. Built on a ViT backbone and trained over 18.7M tiles (~23T pixels) from Sentinel-1 SAR and Sentinel-2 optical imagery, TerraFM unifies modality-specific inputs using:
- 🧩 Modality-specific patch embeddings
- 🌀 Adaptive cross-attention fusion
- 🎯 Dual-centering regularization for long-tailed distributions
TerraFM sets a new benchmark on GEO-Bench and Copernicus-Bench, demonstrating strong generalization across geographies, modalities, and tasks — including classification, segmentation, and landslide detection.
- Multimodal Pretraining: Uses Sentinel-1 (SAR) and Sentinel-2 (L1C, L2A) as natural augmentations.
- Large-Scale Dataset: Trained on 18.7M global tiles from the Major-TOM dataset.
- Cross-Attention Fusion: Dynamically aggregates information across sensors at patch level.
- Dual-Centering: Mitigates long-tailed land cover bias using ESA WorldCover statistics.
- Benchmark SOTA: Outperforms prior FMs (Galileo, Prithvi, DOFA) across multiple EO tasks.
Overall architecture of TerraFM. It unifies student-teacher contrastive framework with modality augmentation with cross-attention fusion, and a new dual centering regularization. TerraFM is founded on ViT backbone and is trained on 18.7M globally distributed samples for pre-training and utilizes large-tile inputs for encoding broader spatial context. For illustration, RGB channels from S2-L2A and S2-L1C are selected, and S1 is visualized using a false-color RGB composite.
| Model | Modality | Input Size | Backbone | Link |
|---|---|---|---|---|
| TerraFM-B | Sentinel-1 RTC + Sentinel-2 Level 2A + Sentinel-2 Level 1C | 224×224 | ViT-Base | Download |
| TerraFM-L | Sentinel-1 RTC + Sentinel-2 Level 2A + Sentinel-2 Level 1C | 224×224 | ViT-Large | Download |
TerraFM can be used directly via the terrafm.py module, which provides standalone implementations of the TerraFM-Base and TerraFM-Large models for easy integration into any codebase.
from terrafm import terrafm_base, terrafm_large
import torch
# Simulated input: 1 sample, 12 channels, 224×224 resolution (e.g., Sentinel-2 L2A)
x = torch.randn(1, 12, 224, 224)
# Load TerraFM-Base model
model = terrafm_base()
# Load pretrained weights (e.g., TerraFM-B.pth)
state_dict = torch.load("TerraFM-B.pth", map_location="cpu")
msg = model.load_state_dict(state_dict, strict=False)
# Forward pass
y = model(x)
print(f"Output shape: {y.shape}")We evaluate image classification using k-nearest neighbors (kNN) and report Top-1 accuracy for all single-label tasks. For the multilabel BigEarthNet benchmark, we report the F1 score.
| Model | Backbone | m-EuroSat (100%) | m-EuroSat (1%) | m-BigEarthNet (100%) | m-BigEarthNet (1%) | m-So2Sat (100%) | m-So2Sat (1%) | m-Brick-Kiln (100%) | m-Brick-Kiln (1%) |
|---|---|---|---|---|---|---|---|---|---|
| SatMAE | ViT-Base | 84.1 | 34.8 | 50.6 | 29.0 | 36.0 | 23.1 | 86.1 | 73.5 |
| SatMAE++ | ViT-Large | 82.7 | 48.5 | 50.8 | 31.6 | 34.7 | 23.4 | 89.6 | 76.7 |
| CROMA | ViT-Base | 85.6 | 51.3 | 58.8 | 44.7 | 48.8 | 33.8 | 92.6 | 85.1 |
| SoftCon | ViT-Small | 89.8 | 27.2 | 64.7 | 43.3 | 51.1 | 31.4 | 89.2 | 77.8 |
| DOFA | ViT-Base | 82.8 | 49.6 | 49.4 | 29.9 | 41.4 | 29.4 | 88.3 | 78.3 |
| Satlas | Swin-Tiny | 81.7 | 35.8 | 51.9 | 29.6 | 36.6 | 27.1 | 88.2 | 73.0 |
| MMEarth | CNN-atto | 81.7 | 30.0 | 58.3 | 39.6 | 39.8 | 25.1 | 89.4 | 79.7 |
| DeCUR | ViT-Small | 89.0 | 46.6 | 63.8 | 49.6 | 45.8 | 30.9 | 83.7 | 74.2 |
| AnySat | ViT-Base | 82.2 | 47.1 | 54.9 | 33.7 | 39.8 | 29.0 | 85.3 | 72.0 |
| Galileo | ViT-Base | 93.0 | 56.6 | 59.0 | 36.5 | 54.8 | 43.2 | 90.7 | 78.0 |
| Prithvi-2.0 | ViT-Large | 80.2 | 48.0 | 49.4 | 28.8 | 29.5 | 26.1 | 87.9 | 80.6 |
| Copernicus-FM | ViT-Base | 76.0 | 47.4 | 53.8 | 33.3 | 38.4 | 23.3 | 93.0 | 83.2 |
| TerraFM | ViT-Base | 94.2 | 59.3 | 68.7 | 49.4 | 55.1 | 41.6 | 94.5 | 85.6 |
| TerraFM | ViT-Large | 95.1 | 62.1 | 69.4 | 50.6 | 55.9 | 41.1 | 93.0 | 82.2 |
Comparison of TerraFM with existing supervised and self-supervised methods on Copernicus-Bench.
Metrics include OA (Overall Accuracy), mAP (mean Average Precision), and mIoU (mean Intersection over Union).
| Dataset | Metric | Supervised | Random | SoftCon | CROMA | DOFA | Copernicus-FM | TerraFM |
|---|---|---|---|---|---|---|---|---|
| Backbone | -- | ViT-B/16 | ViT-B/16 | ViT-B/14 | ViT-B/8 | ViT-B/16 | ViT-B/16 | ViT-B/16 |
| Cloud-S2 | mIoU | 59.4 | 60.4 | 66.9 | 65.0 | 65.0 | 66.7 | 67.9 |
| EuroSAT-S1 | OA | 81.5 | 75.4 | 83.6 | 83.9 | 81.7 | 87.2 | 87.8 |
| EuroSAT-S2 | OA | 97.6 | 92.5 | 96.7 | 97.0 | 97.2 | 97.9 | 99.1 |
| BigEarthNet-S1 | mAP | 70.6 | 63.8 | 78.7 | 70.8 | 70.5 | 77.9 | 76.9 |
| BigEarthNet-S2 | mAP | 80.1 | 71.6 | 83.6 | 76.4 | 75.5 | 79.0 | 84.4 |
| DFC2020-S1 | mIoU | 50.8 | 45.4 | 52.8 | 52.7 | 49.7 | 52.4 | 55.4 |
| DFC2020-S2 | mIoU | 66.2 | 62.3 | 64.1 | 66.5 | 61.8 | 64.5 | 63.8 |
| LCZ-S2 | OA | 85.3 | 77.4 | 83.6 | 84.1 | 83.0 | 84.4 | 87.0 |
Performance comparison on GEO-Bench for both classification (Top-1 Accuracy), segmentation (mIoU), and F1 score (for m-BigEarthNet).
TerraFM achieves state-of-the-art results across multiple datasets, outperforming previous foundation models.
| Method | Backbone | m-EuroSat | m-BigEarthNet | m-So2Sat | m-Brick-Kiln | m-Cashew-Plant | m-SA-Crop-Type |
|---|---|---|---|---|---|---|---|
| SatMAE | ViT-Large | 96.6 | 68.3 | 57.2 | 98.4 | 30.8 | 24.8 |
| SatMAE++ | ViT-Large | 96.5 | 67.9 | 56.0 | 98.6 | 29.6 | 25.7 |
| CROMA | ViT-Large | 96.6 | 71.9 | 60.6 | 98.7 | 31.8 | 32.0 |
| SoftCon | ViT-Base | 97.5 | 70.3 | 61.7 | 98.7 | 29.6 | 30.8 |
| DOFA | ViT-Large | 96.9 | 68.0 | 58.7 | 98.6 | 27.7 | 25.4 |
| Satlas | Swin-Base | 97.5 | 72.8 | 61.9 | 98.9 | 25.1 | 23.4 |
| MMEarth | CNN-atto | 95.7 | 70.0 | 57.2 | 98.9 | 24.2 | 22.2 |
| DeCUR | ViT-Small | 97.9 | 70.9 | 61.7 | 98.7 | 26.2 | 21.5 |
| Prithvi 2.0 | ViT-Large | 96.5 | 69.0 | 54.6 | 98.6 | 26.7 | 22.9 |
| AnySat | ViT-Base | 95.9 | 70.3 | 51.8 | 98.6 | 26.1 | 27.1 |
| Galileo | ViT-Base | 97.7 | 70.7 | 63.3 | 98.7 | 33.0 | 30.1 |
| TerraFM | ViT-Base | 98.1 | 72.6 | 64.9 | 98.7 | 34.1 | 33.0 |
| TerraFM | ViT-Large | 98.6 | 73.1 | 66.6 | 99.0 | 37.2 | 34.5 |
Landslide detection performance on the Landslide4Sense test set.
Despite having significantly fewer parameters (120M vs. 300M), TerraFM achieves higher overall segmentation performance, especially for landslide regions.
| Model | mIoU | IoU (Landslide) |
|---|---|---|
| Prithvi-EO-2.0 (300M) | 65.0 | 31.5 |
| TerraFM (120M) | 70.8 | 43.1 |
If you find our work and this repository useful, please consider giving our repo a star and citing our paper as follows:
@article{danish2025terrafmscalablefoundationmodel,
title={TerraFM: A Scalable Foundation Model for Unified Multisensor Earth Observation},
author={Muhammad Sohail Danish and Muhammad Akhtar Munir and Syed Roshaan Ali Shah and Muhammad Haris Khan and Rao Muhammad Anwer and Jorma Laaksonen and Fahad Shahbaz Khan and Salman Khan},
year={2025},
eprint={2506.06281},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2506.06281},
}If you have any questions, please create an issue on this repository or contact at muhammad.sohail@mbzuai.ac.ae.