Jun-09-25: 🚀 Initial release of TerraFM codebase and pretrained models
Jun-09-25: 📄 Paper released on arXiv: arxiv link. 🔥🔥
🌍 Overview
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.
🔬 Key Features
TerraFM Architecture
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.
🧱 Architecture
TerraFM Architecture
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.
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.
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
🛰 Copernicus-Bench
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
🧪 GEO-Bench Performance
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 (Landslide4Sense)
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
Landslide Detection
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📜 Citation
If you find our work and this repository useful, please consider giving our repo a star and citing our paper as follows:
bibtex
1@article{danish2025terrafmscalablefoundationmodel,
2 title={TerraFM: A Scalable Foundation Model for Unified Multisensor Earth Observation},
3 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},
4 year={2025},
5 eprint={2506.06281},
6 archivePrefix={arXiv},
7 primaryClass={cs.CV},
8 url={https://arxiv.org/abs/2506.06281},
9}
📨 Contact
If you have any questions, please create an issue on this repository or contact at muhammad.sohail@mbzuai.ac.ae.
