TerraFM: A Scalable Foundation Model for Unified Multisensor Earth Observation
Authors: Muhammad Sohail Danish
Muhammad Akhtar Munir
Syed Roshaan Ali Shah
Muhammad Haris Khan
Rao Muhammad Anwer
Jorma Laaksonen
Fahad Shahbaz Khan
Salman Khan
Venue: ICLR 2026 Poster (2026)
Keywords: Remote Sensing
Foundation Model
Geospatial
Primary area: foundation or frontier models, including LLMs

Abstract: Modern Earth observation (EO) increasingly leverages deep learning to harness the scale and diversity of satellite imagery across sensors and regions. While recent foundation models have demonstrated promising generalization across EO tasks, many remain limited by the scale, geographical coverage, and spectral diversity of their training data, factors critical for learning globally transferable representations. In this work, we introduce TerraFM, a scalable self-supervised learning model that leverages globally distributed Sentinel-1 and Sentinel-2 imagery, combined with large spatial tiles and land-cover aware sampling to enrich spatial and semantic coverage. By treating sensing modalities as natural augmentations in our self-supervised approach, we unify radar and optical inputs via modality-specific patch embeddings and adaptive cross-attention fusion. Our training strategy integrates local-global contrastive learning and introduces a dual-centering mechanism that incorporates class-frequency-aware regularization to address long-tailed distributions in land cover. TerraFM achieves strong generalization on both classification and segmentation tasks, outperforming prior models on GEO-Bench and Copernicus-Bench. Our code and pretrained models will be publicly released.

Published as a conference paper at ICLR 2026
TERRAFM: A SCALABLE FOUNDATION MODEL FOR UNIFIED MULTISENSOR EARTH OBSERVATION
Muhammad Sohail Danish
Muhammad Akhtar Munir'
Syed Roshaan Ali Shah?
Muhammad Haris Khan
Rao Muhammad Anwer
Jorma Laaksonen
Fahad Shahbaz. Khan Salman Khan 1,5
Mohamed bin Zayed University of Artificial Intelligencee University College London
8 Aalto University
*Linköping University, Sweden
Australlia National University
ABSTRACT
Modern earth observation (EO) increasingly leverages deep learning to harness the scale and diversity of satellite imagery across sensors and regions. While recent foundation models have demonstrated promising generalization across EO tasks, many remain limited by the scale, geographical coverage, and spectral diversity their training data, factors critical for learning globally transferable representations. In this work, we introduce TerraFM, a scalable self-supervised learning model that leverages globally distributed Sentinel-1 and Sentinel-2 imagery, combined with large spatial tiles and land-cover aware sampling to enrich spatial and se- mantic coverage. By treating sensing modalities as natural augmentations in our self-supervised approach, we unify radar and optical inputs via poddality-specffie patch embeddings and adaptive cross-attention fusion. Our training strategy in- tegrates local -gcal-global contrastive learning and introduces dual-centering mecha- nism that incorporates class- teesss-requeec-aavee regularization address long-tailed distributions in land cover. TerraFM achieves strong generalization on both clas- sification and segmentation tasks, outperforming prior models on GEO-Bench and Copernicus-Benchh Our code and pretrained models are publicly available https//ggtuub.coo/mmzzaoooorrrot
INTRODUCTION
Earth Observation (EO) provides systematic measurements of the surface of earth, supporting
a wide spectrum of critical applications such as land use monitoring (Wang et al. 2023 crop
evaluation (Prodhan et al., [021 Kussul al. (017)) urban development (Yu & Fang 2023).
and disaster response (Huot et al.) 2022 Sarkar et al. 2023 Rahnemoonfar et al. 2021). These
capabilities are enabled by a growing fleet of EO satellites, most notably the Sentinel missions, which deliver multi-modal, multi-temporal data global scale (Torres al. 2012 Drusch et al.) 2012). The rise of deep learning. particularly deep neural networks (DNNs). has fundamentally reshaped the way EO data is processed and interpreted (Wang al. 2025 Szwareman al. 2024
Tseng et al. 2025). Modern DNNs enable automated extraction of spatial and semantic patterns from raw imagery, driving downstream tasks such scene classification, object detection, and semantic segmentation (Astruc et al.) 22024; Waldmann et al.) (2025] Kuckreja et al. 2024 Tseng et al. 2025 Fuller al. 2023). These models offer scalable alternative traditional hand-engineered
pipelines by learning generalizable representations directly from data (Guo et al. 2024a). As EO data continue expand in scale, diversity, and complexity, DNNs have become foundation for building
high-capacity models capable of generalizing across geographies, modalities, and tasks (Szwarcman
et al. 2024 Astruc et al., 2024 Waldmann al. 2025).
Remote sensing data is inherently multimodal, comprising diverse sensor types such optical, SAR, and multispectral imagery. Traditional EO pipelines often focus on mingle-modality inputs, typically high-resolution optical imagery, limiting the model's ability to generalize across varying sensing conditions. In contrast, multimodal and multispectral data sources, such as Sentinel-1 SAR and Sentinel-2 information, enabling richer scene understanding (Han et al.) 2024) Fuller et al. (202).. Foundation models that embrace this diversity have demonstrated superior transferability across tasks and
optical bands, capture complementary structural and spectral

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Published as a conference paper at ICLR 2026
geographies (Teng al. 2025 Guo et al. 2024a). However, variation in ground sampling distance (GSD) across EO data makes tile size a critical factor, smaller tiles capture local detail but risk overfitting to texture, while larger tiles provide broader semantic context but require scale-robust architectures (Reed et al. 2023). Recent works like AnySat (Astruc et al. (024) and msGFM (Han et al. 2024) have shown that scale-invariant modeling and mixed-resolution pretraining lead to more robust and generalizable representations. Crucially, large-scale sampling across geographies and resolutions enables EO foundation models to learn invariant features across sensors and global conditions.
As EO foundation models scale accommodate diverse sensor inputs and resolutions, two dominant pretraining paradigms have emerged: masked autoencoders (MAE) and contrastive learning. Although MAES focus on reconstructing the spatial structure, their reliance on RGB-centric ViTs limits their adaptability to multispectral or SAR inputs with varying spectral dimensions (Li li et al.. 2024
Szwareman et al. (2044). In contrast, contrastive approaches such as DINO (Caron et al. ||,,|22211 Oquab
al. 2023) and its adaptations to remote sensing (Tseng al. 2025 Fuller et al. 2023 Waldmann
ct al. 2025) offer modality-agnostic training by aligning global and local views through student-
teacher distillation. However, the expansive spatial coverage of EO datasets introduces new challenges: large portions of satellite imagery are semantically sparse or uninformative, and naïve sampling can lead to representation bias. This requires intelligent sampling that prioritizes semantically diverse regions, guided by land cover priors, for balanced and efficient representation learning.
To address these limitations in standard ViTs, particularly their RGB-centric design, lack of modality awareness, and unimodal self- supervision, we introduce TerraFM, unified foundation model tailored for remote sensing. First, we propose a Modality-Specifff Patch Em- bedding module, which replaces the shared pro- jection in standard ViTs with modality-awand embeddings adapted to multispectral and SAR data. This enables flexible handling of sensor- specific spectral profiles while preserving spatial structure. To enhance inve-iivvriance and cross- view consistency, we adopt multi-crop learning within a self-supervised teacher-student frame- work (Caron et al., 202I), promoting robust rep- resentation learning through global-local align- ment. Further, we interpret different aligned modalities (S1-SAR, S2-LIC, S2-L2A) as com- plementary views of the same scene and intro- duce a Cross-Attention Fusion module that dy- namically aggregates modality-specific tokens using learnable spatial queries. This allows the model to selectively emphasize sensor contribu- tions at each spatial location. While modality- specific embeddings and co-located modalities have appeared in prior work (Fuller al. [2023] Tseng et al., 2025 Bachmann et al. 20022). our contribution lie in unifying them within a single DINO-style multi-crop backbone that treats S1, S2-LIC, and S2-L2A as complementary co-registered views, enabling stronger cross-modal coupling and richer aligmment than separate-enee -ncoorr designs. Moreover, prior multimodal MAE models use cross-attention for decoder-side reconstruction (Guo et al. 2024b), TerraFM performs fusion on the encoder side and treats the fused output as an additional augmented view within a single shared backbone. Finally, to mitigate long-tailes land cover distribution issues prevalent in EO data, intro- duce a Dual Centering mechanism into the distillation process. This leverages WorldCover (Zanagal et al. 2022) derived class statistics compute a frequency-awan center, improving balance across dominant and rare semantic categories without requiring supervised objectives. These challenges highlight the need for a unified multimodal framework that scales effectively across sensors and
Performance across GeoBench
ri.Rundat
.012
rediy Nob-
Orp Type
AnySit
Ga FOM
Soros
DOFA
3.0
TerFFM
Figure 1: Performance comparison across GEO- Bench classification tasks using supervised fine- tuning and kNN evaluation. Five recent EO foundation models: AnySat (Astruc al., 2024). DOFA (Xiong et al.) 20224) Galileo (Tseng et al. 2025), Prithvi-EO-v2.00 (Szwarcman et al. 2024), and Satlas (Bastani et al. 2023) are compared against our TerraFM, which consistently outper- forms them across modalities and evaluation set- tings, demonstrating strong generalization.
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Table 1: Comparison of recent remote sensing foundation models across modalities, scale (number of pretraining samples), and benchmarks. TerraFM uniquely blends large tile size, WorldCover- informed metadata, and global-scale training (18. 7M samples) with evaluation on both GEO-Bench and Copernicus-enchh
Model
Modalities
Scale
Resolution
TileSize
Metadata
Benchmarks
Pixels (-T)
SarMAEs
82, RGB
~1,2 M
ID-60 m
224, 96
No
6DS
0.12
Galiãco
ESA
~v3100.9
GBD+5
1.58
CROMA
S1
~1 M
10
96, 120
No
7DS
0.98
SoftCon
S1,
~0.78 M
10mm
224
Yes
4EO + DS
0.76
AnySat
Acrial, S1/S2, MODIS,
M
0:2-250
10-240
No
11
Priltv--EO-2.00
S2, HLS
4.2 M
m0
224
Yes
GEO + SME
5.06
DOFA
S1, S2, EnMAP etc.
~8 M
1-30 m
512, 128
Yes
GEO + DS
6.74
Panopticon
S2, WV23, NAIP
~2.6
0.3-100
96,
Yes
GBD
2.34
MMEarth
S1, S2. DEM, dtc.
~
0,3-100
128
Yes
GEO
0.51
msGFM
RGB, S2, SAR. DSM
~2M
0.1-30 m
192
No
DS
0.44
CMpemizus-PM
S1-S5P, DEM
18.7
m-1
Mixed
Yes
Cop-Rench
5.12
TerraEM (Ours)
S1, S2 LICIL2A
18.7 M
10-60 m
S34
Yes
GEO Cop-Bench
23,32
resolutions. Our approach, TerraFM, directly addresses these issues as shown in Figure I achieves superior performance compared to recent EO foundation models. Our key contributions are:
Contributions: (1) modality-seccific patch embedding mechanism is introduced to generalize ViTs across heterogeneous remote sensing modalities with varying spectral dimensions. (2) We treat sensor modalities as natural augmentations and introduce a cross-aftention fusion block that unifies multi-modal inputs within a shared encoder. (3) To address long-taiie ong-tailed LULC distributions, dual- centering strategy incorporated to regularize representation learning using class-frequed maas-frequency-awam statistics. (4) Extensive experiments on GEO-Bench and Copernicus-Bencl demonstrate leading performance across multiple downstream tasks using globally distributed data.
2 RELATED WORK
2022 have become a popular choice for self- supervised pretraining in remote sensing by reconstructing masked image regions using ViT (Doso- Vitskiy et al. 202T). Variants like Scale-MAE (Reed et al. 2023) and MC-MAE (Gao et al. 2022) enhance robustness across spatial scales via scale-aware encodings and convolutional tokenizers. However, MAEs struggle to scale to multisensor EO data, as their RGB-centric tokenization and reconstruction objectives limit generalization to multispectral and SAR modalities with diverse chan- nel structures (Xie et al., 2023 Li et al. 2024). Unlike MAEs, self-supervised contrastive learning focuses on learning discriminative representations by comparing semantically similar and dissimilar views.
Self-supervised Pretraining: MAES (He et al.
Remote sensing approaches Tang et al. (2023): :: Fuller et al. (2023) Waldmann et al. (2025) leverage
spatial and spectral augmentations create diverse yet consistent views. CROMA (Fuller et al. 2023)
combines contrastive and masked autoencoding losses, while Cross-Scale MAE (Tang et al. 2023)
blends generative and contrastive objectives for multi-scale learning. student-teacher frameworks like DINO (Caron et al. 2021 Oquab et al.] 2023) scale contrastive learning via EMA-updater teachers and global-local view alignment with centering to prevent collapse. These strategies well-suited for EO, where multimodal imagery can act as natural augmentations, enabling scalable, label-free training and broad generalization.
Remote Sensing FMs: Recent advances in remote sensing foundation models (FMs) have scaled self-supervised learning across architecture types, modalities, training sizes, tile resolutions, and metadata usage (Table 1). Multimodal integration is central to recent FMs like Guo et al. (024a); al.) (2024). SkySense (Guo et al.] (024a) applies contrastive learning to temporal-multimode muulimodal data but requires large-scale compute. CopernicusFM (Wang et al. 2025) fuses Sentinel modalities via metadata-aware networks but faces scaling issues with heterogencous inputs. Panopticon (Waldmann ctt al.) (2025) and AnySat (Astruc et al. (024) align cross-moda views through contrastive training, while Galileo (Tseng et al. (2025) uses shared embeddings for SAR and multispectral fusion. Fus- MAE Miia-TT--iig & Veeravalli 2024) adopts attention- baased fusion without contrastive loss, limiting generalization.
Wang et al. (2025); Waldmann al. (2025) Astruc al. (2024); Tseng et al. (2025);
Prithvi-EO-v2.0 (Szwareman et al., 2024) 4) is restricted to single- optical data with temporal- spatial modeling. DOFA (Xiong et al. 2024), msGFM (Han al. 2024), and AnySat (Astruc et al.)
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Casc
Cose
Fusion
Lesrndd Qardet
Loog R
Cross
Sample
Global Craps
Modality Augnentation
Projector
EMA
Student
Local Crops
Sharpen&
S2013C
Modality Augnentation
Enc
Projector
Softrox
XL
Figure 2: Overall architecture of M..raFM. It unifies student-teacher contrastive framework with modality augmentation with cross- coss-atteenton fusion, and a new dual centering regularization. T 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-LIC are selected, and SI is visualized using false-color RGB composite.
2024) address resolution variability using mixed tile sizes or scale-adaptive designs. Our 534px tiles capture broader spatial context than prior RSFMs. While CopernicusFM (Wang et 2025) and DOFA (Xiong et al. 2024) incorporate metadata, we leverage land cover (LULC) priors for semantically informed learning. Both CopernicusFM and our model are trained on 18.7M samples. (Wang) but ours uses over 23T pixels during pretraining, scaling the 5.IT used by Copernicus-Preteet af., (2025) over 4x. This growing body of work makes clear that the next step is moving beyond single-modality scale-limited pipelines toward unified, globally robust EO foundation models.
3 TERRAFM: A SCALABLE MULTISENSOR FOUNDATIONAL MODEL
Unlike prior remote sensing foundation models, our approach integrates a student-teacher contrastive learning framework with dual centering balance long-tailed classes), (to learn cross-modal invariances), and cross-attention fusion (to aggregate multi-sensor context), as illustrated Figure 2 Built on ViT backbone and trained on 18.7M globally distributed samples using 534 534 tiles, TerraFM captures broader spatial context and generalizes effectively across sensing modalities and geographies, achieving strong results on diverse downstream benchmarks.
3.1 ARCHITECTURE
We use globally distributed remote sensing imagery organized over spatial grid, partitioning the earth's surface into fixed-size tiles (Francis & Czerkawski 2024). Each spatial unit, denoted as s, represents one such grid cell. For each sample, observe set of co-registered EO modalities:
M = (S1, S2-LIC, S2-L2AJ,
where S1 corresponds to Sentinel-1 SAR, and S2-L1C and S2-L2A represent two processing levels of Sentinel-2 optical imagery: Level-IC -top-o--atmosphere reflectance) and Level-2A (bottom-of- atmosphere surface reflectance), respectively. Each modality E M provides a multi-channel image at E H x X xCm, where H and W denote spatial dimensions, and Cm is the number of spectral For example, Sentinel-1 contains two channels (VV and VH polarizations),
channels for modality therefore Cs1 = 2, while Sentinel-2 modalities contain up to 13 spectral bands depending on level and resolution. These modalities treated complementary views of the same location, acting as natural augmentations, which support our training strategy and encourage learning modality- invariant representations. To provide semantic grounding, each sample land use and land cover (LULC) category y(() E {1...., Y), derived from the ESA WorldCover product. These categories reflect coarse semantic classes at a global scale and are used to compute glass-feequency-wwar statistics for balanced representation learning.
assigned high-level
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Vision Transformer Model:
VITs adapt the transformer architecture visual data by treating an image as a sequence of patch tokens instead of a dense pixel grid. A typical VIT consists of two main components: patch embedding module and transformer encoder. Given an input image a E IR. H xXxX the patch embedding layer fo divides the image into N non-overlapping patches of size P P P. and projects each patch into d dimensional embedding:
{z))==1 = fo(x))
2, ER..
This projection is typically implemented using a convolutional layer with kernel size and stride equal to the patch size P. parameterized by weights Wa E ædxCxPXP, To encode spatial information, the transformer encoder augments each patch token 2; with positional vector. A learnable class token eets is added to the sequence, which yields the full input:
z [csss Zelsi
{z. + pos; ]
The token sequence Z is processed by a stack of L. transformer layers, denoted Encg. For classification tasks, only the final class token žels is forwarded to a prediction head.
-Sdality-Specific Patch Embedding: Standard patch embedding layers in ViTs are typically implemented using shared convolutional projection across all inputs, making it unsuitable for multi-modal remote sensing data. To better handle this heterogeneity, we adopt a modality-specific patch embedding strategy. For each modality m .M, we define an embedding function Se... that sequence of patch tokens (m)) Nx where C.
maps the imput image (!n) E IR H xxx XCm is the number of channels and N is the number patches. Each fe.,, is parameterized independently to account for modality-specifff dynamics. We associate each modality with a learnable embedding vector e (m)) E D. This vector is added to every token from that modality via broadcasting:
(m) (m)) + IN (E (e())))
where 1N E R is vector of ones. This allows the model to distinguish between modalities while preserving local spatial and spectral features. Finally, to enable shared processing in the Transformer
encoder, the enriched tokens (mm) are linearly projected into a common latent space of dimension d
using a shared projection vp R + Rdd:
((m)
v 7 (m))
I Nxd
This operation aligns all modality-speciff token sequences in a unified representation space, allowing the encoder to process them jointly.
Modality Augmentation and Cross-Attention Fusion: Remote sensing observations of a single location are often captured using multiple sensors, each providing a unique spectral or radiometric perspective. Instead of treating these modalities as independent inputs, we interpret them as com- plementary views of the same scene. This allows us to use modality diversity as a form of natural augmentation, enabling the model to learn sensor-invariant representations. In our setup, each spatial sample from the Major-TOM dataset (Francis & Czerkawski] 2024) is observed via a fixed set of modalities. During pretraining, we independently assign modalities to the student and teacher networks via stochastic selection (threshold = 0.5), ensuring cross-modal supervision. E.g., the teacher may observe global crop from Sentinel-1, while the student receives local views from Sentinel-2 L2A. This modality augmentation strategy encourages the model to align features across sensors, improving robustness sensor-specific artifacts. We consider two cases based on the number of selected modalities:
Single-Modality Views: If only one modality is selected, the input is passed through the corre- sponding -odality-specific patch embedding layer followed by the shared transformer encoder. This follows the standard ViT pipeline but uses modality-awee aware embeddings handle spectral channel differences. 2) Multi-Modality Fusion via Cross-Attention: When multiple modalities M C M are selected, we activate a modality fusion module based on cross-attention. For each selected modality m E M, we obtain patch token sequence (m) DN where N is the number of spatial positions. These are stacked into a tensor Zan E xx aligning spatial positions across modalities.
For each position n = 1...., N, we define shared learnable queries q E N2 which attend to modality-specific keys K, E IR MM x and values V, E Mx where, Kn, and V, are obtained via separate learned linear projections of modality-speciff tokens, yielding N,, intermediate outputs:
Mult Mult:HHeadkttenttionl K,, V.) E R,DD
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To aggregate them, compute learned weighted mean using softmax- attention scores:
w = Sof Softmax(=/, pr), fused
Σ wy b
where p, IR Dx1 is learnable projection for scoring the query outputs. This results fused token fused E RD. The final sequence Zused Nx is then passed to the shared encoder Enc This cross-attention fusion allows the model dynamically weigh the modality contributions each spatial location, capturing diverse information while maintaining spatial coherence. For clarity, the learnable queries are shared across all spatial locations, and fusion yields one token per location, preserving the ViT backbone's 's original sequence length.
3.2 PRETRAINING
Our pretraining strategy builds on the DINO framework, which performs self-supervised learning. operates using teacher-student setup, where both networks share the same ViT backbone and lightweight three-layer projection head. Let ge, and ge, denote the student and teacher networks, respectively. While the student trained using gradient-based optimization, the teacher is updated using EMA of the student's weights:
1+ cos (Te/E)
0. -- da O2 + - Ao) 1- (
2
where e is the current epoch, E. is the total number of training epochs, and Åo E [0.996, 1) is the initial momentum coefficient. The cosine schedule gradually increases Åe, stabilizing the teacher updates training progresses. This EMA mechanism allows the teacher to serve temporally smoothed ensemble of past student states, yielding more stable and consistent targets.
To enable scale-invariant and cross-view representation learning, adopt multi-crop strategy as used in DINO (Caron et al. 202T]. For each input sample, we generate two (1) () Xy and J low-resolution local crops {- 1==1 X₁. high-resolution global crops {>/ Fg
Multi-Crop Learning:
The teacher network processes only the global crops, while the student receives both global and local
views. Each network produces K K-dim output which is temperature-scaled and normalized via the
softmax function:
exp(ge, (z)) ΣK -==1 exp(so, (ar (x)) (R)/TT))
expl(eo, (a)) ((1)) (()) Σ exp((go, (k:) c(i))/T))
(x))
(x)
where т, and 7r temperature parameters that control output sharpness, and R K a centering term representing the running mean of teacher logits, used to stabilize training and avoid representation collapse. The centering term is updated using an exponential moving average over the teacher outputs:
B
c Bc + (1 3)
1
Σ
go. (z.).
where 3 E [0.9, 0.999) controls the momentum, and B is the batch size. The overall loss encourages consistency between teacher and student predictions across all distinct view pairs:
Σ
Σ
LEE (2(()),
x x' ''
where X' Ng U Xe, and Cce(-s) :) denotes the cross- coss-entropp loss. This loss formulation requires the student to produce consistent representations in all views.
Dual Centering for Long-Tailed Distributions:
Remote sensing datasets often exhibit long-tailed distributions of LULC classes, with frequent categories such as Forest dominating, while classes like Urban or Bare Land remain underrepresented shown in Figure B] This imbalance persists after subsampling and poses challenges for representation learning. Standard self-supervised approaches like DINO (Caron et al.) (02T)) apply a single global centering term to stabilize training and avoid representation collapse, but they do not

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Published as a conference paper at ICLR 2026
account for semantic imbalance in the data. To address this, propose dual-centering scheme that combines global statistics with class- fresueeee-uueuann aware regularization. In addition to the standard global center vector e, we introduce a secondary center O₁ R computed from subset of samples belonging to high-frequency LULC classes, such as tree cover, grassland, and open seas, based on dataset-level statistics. Given batch of teacher logits go, (a:), the adjusted logits for training computed as:
g x = go, (x) - a..ca.c.. c-c.ccca (1 (1-a)- Ch.
where a E [0, 1] balances the contribution of the global and frequency-award centers. The vector On is updated via exponential moving average using only frequent-class samples within each batch. This dual-cent mechanism serves two key purposes: (i) it preserves the stability benefits of global centering as in DINO, and (ii) it introduces a soft rebalancing bias that counteracts the overrepresentation of dominant classes in the feature space. In ablations (Table this adjustment leads to more balanced representation learning and improved downstream performance, particularly for underrepresented LULC categories.
PRETRAINING DATA SAMPLING
We utilize Major-TOue TOM dataset (Francis & Czerkawski, 2024) as our primary EO source for pertaining. It contains 2.24 million globally distributed grid cells, each spanning approximately 10.68 km x 10.68 km (=114 km²), and provides tri-modal, co-registered imagery from Sentinel-2 Level-IC, Sentinel-2 Level-2A, and Sentinel-1 RTC. MOMM stands out as one of the few publicly available datasets offering dense multi-modal coverage at global scale. Each cell independently selects random 4- month window before cloud screening, which helps limit systematic seasonal and regional bias. However, over one- third of its samples lie outside a 10 km terrestrial buffer, often within the Open Oceans class (Zanaga al.. 2022). limiting their relevance for land-centric tasks. Motivated by insights from Roscher et al. (2024), which emphasize the importance of semantically rich samples, and Wang jet al. (2024d), which highlight the utility of structural pri- ors, we applied principled filtering strategy. Specifically.
3/5
sigutatto
voter
Strubland
Open
(100%
Smon Wetlond
12.2%
Figure 3: Land-use/land-cover (LULC) breakdown across the training tiles. A number of semantically important classes (e.g., builtup, mangroves, ice) remain underrepresented due to skewed data distribution.
we removed 98% of ocean-classifiee tiles (retaining 2% to preserve marine representation) and sampled the terrestrial subset using global distributional priors across land cover (Zanaga et al., 2022). climate zones (Beck et al. 2018). and ESRI world regions (Esri et al., 2025). Global distributional priors refer specifically to the majority land-cover label for each grid cell, which we retain only to compute class-frequency statistics for the dual-centering regularizer; this metadata is not provided model input used supervision. This approach emphasizes meaningful land regions with 3 ecological variety. Figure shows the global coverage of the tile and Figure A4 in the Appendix covers the map. For pretraining, we curated filtered subset of over 1.5 million grid cells with consistent coverage across all three modalities (S1. S2-LIC, and S2-L2A). Each 10.68 km x 10.68 km grid cell was divided into four non-overlapping tiles of 534 534 pixels, resulting in more than 6 million tiles per modality. In total, this yielded 18.7 million boddality-secciffe training tiles. During training, modalities were stochastically sampled and treated as natural augmentations to promote sensor-invariant representation learning. To mitigate spatial sampling bias and support semantically-aware learning, we enriched each grid cell with metadata from the ESRI World Regions dataset (Esri et al.) 2025).
EXPERIMENTS AND RESULTS
5.1 PRETRAINING IMPLEMENTATION DETAILS
We pre-train TerrFFM using 534 534 tiles as inputs. Following the DINO-style cropping strat- egy (Caron et al. 202T). each tile is randomly cropped at two scales: (i) global crops, sampled with ratios in (0.25, 1.0 of the tile size and resized to 224 224, and (ii) local crops, sampled with
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Table 2: 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 Fl score. Results other than Copernicus- and TerraFM are directly taken from Tseng al. (025). Bold indicates the best result, and underlining denotes the second-best.
m-EaonSar Training
mn Bighatiai Training %
m--o22aat Training %
Hrick-KKlle Triinnn
Model
Backhose
1000
100%
1007
SaMAA
Cuncelal
SaMAE-T
ViE-Large
23.4
89.6
CROMA
Phlle Fin
ViT-Reee
13.8
92.6
85.1
SofCCo
VIT Small
89.8
27.2
61.7
43.3
51.1
31.8
89.2
77.8
DOFA
Kinng e
A044
ViT-Base
82.8
49.6
49.4
29.9
41.4
29,4
88.3
78.3
Stlaas Bapuin 01 11
Swin-Tiny
$1.7
35.8
51.9
79
36.6
27.1
88.2
73.0
MME
Necuunza
11
2024
CNN anto
81.7
30.0
58,3
39.6
39.8
25.1
89.4
79.7
DeCUR
(Wing
A1/45
VIT-Small
89.0
26.6
63.8
42.6
45.8
30,9
83.7
74,2
AnySat
Bor-Base
82.2
47.1
54.9
33.7
38.8
29.0
85.3
72.0
Galllco
Base
43.2
90.7
Prithss
0024
VIT- Lrgo
87.9
Copemicus
Warg
23.3
930
TermFM (Ours
ViT-Large
ViT-Rese
95.1
62.1
50.3
68.7
69.4
50.6
49.4
35.9
55.1
41.6
11.1
94.5
93.0
82.2
Table 3: Performance comparison on GEO-Bench for both classification (Top-1 Accuracy), seg- mentation (mloU), and Fl score (for m-BigEarthNet). TerraFM achieves state-of-the results across multiple datasets, outperforming previous FMs. Bold indicates the best result, and underlining denotes the second-best.
Classification
Segmentation
Rackhone
m-FunoSat
miRigFurtheet
n9-5o25at
m-Brick-Klln
m--Cishee-Ploott
m-SA-Ciio--T Type
Sa:MAE
LirgLarge
96.6
68.3
57.1
98.4
30.8
24,8
SurMAE4
ViT-Large
96.5
67.9
56.0
98.6
79.6
25.7
CROMA
Var-Large
96.6
71.9
60.6
98.7
31.8
32.0
SoftCon
ViT-Rese
97.5
70.3
61.7
98.7
29.6
30.8
DOFA
VirgLarge
96.9
68.0
$8.7
98.6
27.7
25,4
Sutlas
Swin-Base
98.4
23.4
MMBEanth
CNN-atio
DeCUR
ViT-Small
07.9
98.7
26.2
Pribsi-EO-2e +2.0
Large
96.5
69.0
$4.6
98.6
26.7
22.9
AnySat
ViT-Bise
95.0
70.3
51.8
98.6
26.1
27.1
Gallleo
ViT-Base
97.7
70.7
63.3
98.7
33.0
30.1
TaraFM
Larg
ViT-Base
98.6
98.1
72.6
64.9
99.0
98.7
34.1
37,0
34,6
32.8
ratios in [0.05, 0.25] and resized to 96 x 96. All inputs are then tokenized with a 16 x 16 patch resolution. The training dataset comprises around 1.53 million multi-modal samples, from which we define a virtual epoch of 300K samples to ensure frequent parameter updates. TerraFM-B is trained for 150 epochs and TerraFM-L. for 200 epochs, each with linear warmup over the first 30 epochs. Models are trained on 64 GPUs, TerraFM-B training takes 92 hours with batch size of 1024, while TerraFM-L uses a batch size of 2048 and trains for 183 hours. The learning rate is linearly scaled with batch size, initialized as Ir 0.0001 batch size /2zz/ Following DINO-style pretraining, disable batch normalization in the projection head and freeze the last layer the student for the first 3 epochs to stabilize early training. The output dimensionality is set K 65,536, with the teacher temperature linearly increasing from 0.04 to 0.06 over the first 50 epochs. The teacher momentum follows cosine schedule starting from 0.996. A drop path rate of 0. is applied for regularization. For modality fusion, we set N₂ 5 and = 0.8 during pretraining.
5.2 EVALUATING DOWNSTREAM TASKS
Benchmarks: We evaluate our model on two comprehensive remote sensing benchmarks: GEO- Bench (Lacoste al. 2023) and Copernicus- (Wang et al., (024a). both of which include diverse downstream tasks spanning multiple domains and modalities. The benchmark datasets are described in Appendix A and for evaluation protocols (linear probing, UperNet probing, k-NN, and fine-tuning), we refer the reader Appendix C.I
We report KNN classification accuracy on four standard GEO-Bende classification tasks to evaluate the quality of learned representations in rrining-frre setting. As shown in Table 2 TerraFM achieves the highest performance across three datasets, outperforming both modality- specific and multimodal foundation models. Notably, our model achieves 95.1 1% on AT and
Discussion:
8

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Published as a conference paper at ICLR 2026
94.5% m-Briic-Kii Kiln, highlighting the effectiveness of the learned representations standard scene classification tasks. On other challenging tasks such as m-So2Sat and m m--BiEaathheet our model achieves leading performance (55.9% and 69.4%, respectively), outperforming Galileo (Tseng et al. 2025), despite So2Sat having fewer channels than used during pretraining, highlighting the model's s robustness missing modality information. Compared to CROMA (Fuller et al. 2023 and
DeCUR (Wang et al. 2024a). our gains suggest that contrastive alignment combined with cross-modal fusion enhances class separability. The results across tasks of varying difficulty indicate that our model learns robust and transferable representations that generalize well across different scenarios.
Further GEO-Bench results with fine-tume ane-tuning and linear probing are reported in Table for clas- sification (with fine-tuning), TerraFM achieves the improvement on m- m- Ni-BBigErrthNet (73.1%) and m-EuroSat (98.6%), and the best-performing model on m-So2Sat (66.6%) For segmentation (with lin- ear probing), our M-rraFM-LL notably outperforms existing models on m-SA-Crop-Tyyee Type (34.6% mloU) and m- mnCaashew-Plan (37.0% mloU). TerraFM-B surpasses larger counterparts such as ViT-Large used in SatMAE++(NNman et al. 2024) and DOFA(Xiong et al. 2024). On the Copernieus-Bench (Wang) cet al. 2025) (T Terral FM delivers state-of-the-ar results across most tasks and modalities. achieves 67.9 mloU on Cloud-S2, 87.8 OA on EuroSAT-SI, and 99.1 OA on EuroSAT-S2, surpass- ing all prior models. On BigEarthNet-S and DFC2020-S1, TerraFM attains 84.4 mAP and 55.4 mloU, respectively, marking clear gains over existing FMs. While SoftCon (Wang al. (202bb) is slightly higher on BigFarthNet-S1 (78.7 vs. 76.9), TerraFM consistently outperforms Copernicus- (Wang et al. 2025) despite sharing the same ViT-B/16 backbone. These results highlight TerraFM's scalability and strong generalization across diverse EO benchmarks.
Table 4: Comparison of TerraFM with supervised and self-supervised methods on Copernicus- Bench. Metrics: OA (Overall Accuracy for classification), mAP (mean Average Precision for
multi-label classification), mloU (mean Intersection over Union for segmentation) Baselines include SoftCon (Wang et al.] 2022bb, CROMA (Fuller et al.. (023), DOFA (Xiong al.] [022], and
Copernicus-IM (Wang et al. 2025). Bold indicates the best result, and underlining denotes the second-best.
Clussifcation
Segmentation
Method
Backbone
EuroSAT-S1
EaroSAT-S2
BigEatthNesSS1 BigHanthNer-S? LCZZ-22
Cloud-S2 DEC2020-81 DEC2020-52
Supervised
VIT-B/16
$1.5
97.6
70,5
80.1
85.3
59.4
50.8
66.2
Random
WT-B/16
75.4
92.5
63.8
71.6
77.4
60.4
45.4
62.3
SoftCon
96.7
83.6
52.8
CROMA
VIT-H/8
83.9
97.0
70.8
76.4
84.1
65.0
52.7
66.5
DOFA
VIT-B/16
81.7
97.2
70.5
75.5
83.0
65.0
49.7
61.8
Copericuu-PM
TerraFM
VTT-B/16
87.8
99.1
76.9
84.4
87.0
67.9
55.4
63.8
5.3 ABLATIONS AND ANALYSIS
Impact of Components: Table highlights the incremental benefits of each component in framework. We train TerraFM-B for 150 epochs on a 200k-sample subset from our full training dataset. To measure the performance on segmentation task, we use uppernet probing and linear probing on the m m-Cashew- -lantttttoinnnii dataset from GeoBench. Adding modality as augmentation improves performance on m-EuroSat by +4.5% and m-BigEarthNet by +3.01%. Incorporating fusion yields large gain on m m-CassewwPlaanattion segmentation by +3.23% with UPerNet probing and +1.4% with linear probing, while dual centering provides further improvements: +0.32% on m- ne-BigErrthNet, +1.9% on m- m-EuroSat, and 18% on m- Deishew-Plantation.. Note that in Table 5 the dual-centering ablation disables only our additional centering term; the standard DINO global centering remains active in all configurations to maintain the stability of the student-teacher training dynamics.
Dual-centering Motivation and Visualization: Here, we discuss the impact of Dual-centering class-wise prediction behavior and representation diversity.
Figure shows that models with Dual-centering exhibit higher softmax entropy across most classes, indicating more calibrated predictions, particularly benefiting rare classes like "Mangroves". Figure 5 reveals that Dual Centering significantly increases prototype diversity, i.c., the number of distinct top-5 features activated, especially for tail classes. This suggests that the model avoids collapsing onto frequent-class prototypes and learns diverse, semantically rich representations. These results

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Published as a conference paper at ICLR 2026
(hepk Usage]
Nean Softras Eobrasy per Glam
Figure 4: Mean entropy per LULC class from Sk train- ing sam- ples, with logits (K = 65, 536) reduced via Gaussian projection. The baseline (no dual-centering) shows lower entropy and overconfident predictions skewed to frequent classes. Dual-centering increases entropy, yielding more balanced predictions, espe- cially for rare classes like Mangroves and Herbaceous-
Figure 5: Prototype diversity is measured the num- ber unique top-5 prototypes across 5k training sam- ples. Dual- centering improves diversity for tail classes like Mangroves, Herbaccous- Wetland, and Built-up, in- dicating richer represen- tations. The baseline reuses fewer prototypes, reflecting over- reliance on dominant frequent-class features.
Wetland.
MAng
DC
Table 5: Ablation components: SS = Self- supervised contrastive learning. MAug Medality Augmentation, Fus Fusion, DC Dual Center- ing. BEN E-BigEarthNee,,
54.62
83.20
50.58
19.4
57 57.63 (+3.01)
57.74 (+0.1))
58.50 (+0.80)
(+.70(+4.50)
62,40 (+3.25)
59.17 (+8.59)
26.2 (-1.4)
(-5.4)
m-EuroSat, CP Cashew Plant. denote results using UPerNet probing while t indicate linear probing. Gains parentheses denote improvements over previous row.
(40.32)
(+1.90)
64.58 (42.18)
(-14
motivate Dual-centering as an effective strategy for reducing class imbalance effects in representation learning.
MACs-Performance Trade-Off: We analyze the accpute-accuracy trade-off using Multi- ply-Accumulate operations (MACs) to measure inference cost. As shown in Figure 6 TerraFM achieves the highest m-EuroSat accuracy at substantially lower MACs, validating the efficiency of our fusion design and pretraining strategy. Notably, models with higher MACs do not guarantee better performance, underscoring the need for compact yet expressive architectures in scalable EO settings. For further results and other supporting information, we refer readers to the Appendix.
Accuracy
Bscibone 2bs
CONCLUSION
In this work, introduced TerraFM, unified and scalable foundation model (FM) specifically designed for multisensor EO. Given the unique nature of EO data, our approach ex- plicitly accounts for sensor heterogeneity, scale-invariance, and frequency imbalance which is critical for building generalizable EO FMs. Our pretraining approach leverages contrastive learning to obtain geographically and spectrally aware representations from large-scale Sentinel-1 and data. patch embed-
600
Specifically. we integrate modality-specinn dings. adaptive cross-attention fusion, and dual-centering contrastive learning objective to enrich the representations on heterogeneous RS data. Our extensive evaluations on demonstrate that Ter-
123
Figure 6: Comparison of model efficiency and accuracy m-EuroSat. We plot the MACs against k-NN classification accu- racy for various baselines. TerraFM mod- els achieve the highest accuracy while maintaining moderate computational cost.
GEO-Bench and Copernicus-Bend raFM consistently outperforms SoTA self-supervised ViT models across both classification and segmentation tasks.
ACKNOWLEDGGENNS
We acknowledge the LUMI supercomputer, owned by the EuroHPC Joint Undertaking and hosted by CSC, and the IUMI consortium, for providing the computational resources that enabled this work.
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Ben G Weinstein, Sergio Marconi, Stephanie Bohlman, Alina Zare, Aditya Singh, Sarah J Graves, and Ethan White. Neon crowns: a remote sensing derived dataset of 100 million individual tree crowns. BioRxiv, 2020.
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Gui-Song Xia, Jingwen Hu, Fan Hu, Baoguang Shi, Xiang Bai, Yanfei Zhong, Liangpei Zhang, and Xiaoqiang Lu. Aid: benchmark data set for performance evaluation of aerial scene classification. IEEE Transactions Geoscience and Remote Sensing, 55(7):3965-3981, 2017.
Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, and Jian Sun. Unified perceptual parsing for scene understanding. In European Conference on Computer Vision. Springer, 2018.
Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Yixuan Wei, Qi Dai, and Han Hu. On data scaling in masked image modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, pp. 10365-10374, 2023.
Zhitong Xiong, Yi Wang. Fahong Zhang, Adam J Stewart, Joëlle Hanna, Damian Borth, Ioannis Papoutsis, Bertrand Le Saux, Gustau Camps- Valls, and Xiao Xiang Zhu. Neural plasticttyinnspired foundation model for observing the earth crossing modalities. arXiv e-prints, pp- arXiv-244 -403,, 2024.
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Jin Z., Lin C., Weigl C., Obarowski J., and Hale D. Smallholder cashew plantations in benin, 2021.
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APPENDIX
This supplementary material presents additional experiments, analyses, and visualizations that complement the main paper. It includes benchmarks information ( detailed descriptions & experiments for our multimodal fusion strategies (B), implementation details (C.1) and evaluations
on scaling trends with data size (C.2) high resolution benchmarks (C.3). & change detection (C.4).
Further more we also provide additional analysis and qualitative figures (.I)). We also report GPU-
hour comparisons with comparable methods (D.2). landslide detection (D.) and visualize the land
cover distribution of our dataset using global maps (D.4).
A BENCHMARKS
We evaluate TerraFM two comprehensive remote sensing benchmarks, GEO-Bench and Copernicus-Bench, which together cover a wide range of tasks, modalities, and resolutions. 1) GEO-Bench (Lacoste al. 2023) standardizes evaluation across 12 curated tasks, including 6 classification and 6 segmentation challenges. These datasets are selected for open access and license compliance, and have been harmonized with consistent evaluation settings. While GEO-Bench supports various sensor types (e.g.. Landsat-8, Sentinel-2, and hyperspectral sensors), for consistency with our model's pretraining, we restrict the main evaluation to Sentinel-2-basse tasks. Specifically, we report results on m-EuroSAT (Helber et al.) (209)). m-BigEarthNet (Sumbul et al. 2019), m- So2Sat (Zhu et al. 2019) m-Brick-Kiln (Lee et al. 2021 (classification) and m- Planttttnannttiion (Z. et al. (021), -ypS--Crop-Type (Foundation) 2027) (segmentation). Additional results on the remaining GEO-Bench datasets, including high-resolution tasks, are provided in Appendix C.3 2)
Copernicus- Berrnhh (Wang et al. 2025) provides 15 downstream tasks aligned with the full Sentinel mission family (Sentinel-T to Sentinel-5P), and categorizes tasks into three levels: low-level (e.g.. cloud detection), mid-level (c.g., land cover segmentation), and high-level (e.g.. flood detection or yield prediction). While Copernicus-B Bereichuus-Bechhhee leverages all Sentinel missions, in this work, we restrict evaluation tasks using only Sentinel-1 and Sentinel-2 imagery. We evaluate the following subset: Cloud-S2 (Aybar et al. 2224), EuroSAT-5/ (Wang et al. 2024c). EuroSAT-52 (Helber et al. (2019). BigEarthWNe-ST (Clasen et al. 2024), BigEarthNer-S2 (Clasen al. 2024). DFC2020-S1
Hääsch
2019), DFC2020-52 (Hääsch 2019). LCZ-S2 (Zhu et al. 2019).
B MULTI-MODAL FUSION STRATEGIES:
We investigate various strategies for multi-modal fusion and report results in Table Al on two benchmark datasets: Eatt and m-EuroSat. As a baseline, we evaluate standard DINO training using only Sentinel-2 1.2A input (DINO (S2-122A)), which learns unimodal representations. To enable explicit modality-aware learning, we apply a modality has its own student and teacher networks, along with an alignment loss between student outputs to enforce cross-modal consistency. This yields consistent gains across both datasets. We also test more expressive fusion approach, CrossAmm (Q 196) Global, where 196 learned queries (standard for 224x224 image inputs) attend globally to multi-modal tokens immediately after patch embedding. However, this method does not perform well, likely due excessive parameterization and lack of inductive bias for spatial alignment. Figure AT visuully summarizes key fusion strategies evaluated in Table A1] including (a)
-eacher approach where cach
(b) unimodal DINO, and (c) CrossAttn (Q= 196) Global, highlighting their architectural differences and fusion mechanisms. Our proposed approach, TerraFM-B (Q = /), treats a modality as an augmentation and performs fusion using a single learned spatial query per location. This lightweight attention mechanism yields the best performance among non-ensemble methods. To further analyze architectural choices, we test a variant, TerraFM-B (ViT PatchEmb), where the convolutional patch embedding is replaced by ViT-S backbone purely for token extraction. While competitive, this setup slightly drops the performance due to increased model complexity and potential overfitting. Finally, our full model, TerraFM- B (Q 5), employs multiple learned spatial queries to achieve richer fusion between modalities. It achieves the best overall performance, validating the scalability and effectiveness of our fusion design.
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Table Al: Ablation study on multi- modal fusion strategies using k-NN evaluation. TerraFM-B with multi- ple spatial queries (Q = 5) achieves the best performance.
m- BigEarthNet
m-EuroSat
DINO (S2-1.2A)
54.6
83.2
Multi-Student-Techhe
55.8
87.8
CrossAttn (Q 196) Global
52.0
77.1
TerraFMBB (Q 1)
57.2
89.2
TerraFM-B (VIT PatchEmb)
56.9
87.2
TerraF--B (Q = 5)
58.1
90.4
(b) DINO
Tear Netwark
(a) Multi- Suatt-Stuuden--ecache
C
Projector
Teacher Network
EMA
Profecter
Stludent Network
EMA
EnCo
|
Projector
Stadont Network
Enco
Projector
(c) CrossAttn 196) Global
Teacher Netwonk
Aligmmnt Loss
Student Network
Ence
Projector
Globel
Enc.
Projector
Student
Teacher Network
Enc.
Projector
Ence
Projector
Globel Crows
Attention
Figure A1: Architectural overview of different fusion strategies: (a) Multi-Student-Teane with alignment loss, (b) unimodal DINO baseline, and (c) CrossAttn (Q 196) with global learned queries.
C EVALUATION
C.I EVALUATION IMPLEMENTATION DETAILS
Linear Probing Evaluation: To evaluate the quality of learned representations, we follow linear probing protocol of 2INOv2(Oquab et al. 2023) that follows with a lightweight grid search over three key hyperparameters: (i) the learning rate, (ii) the number of transformer layers from which features are extracted, and (iii) whether to use only the [CLS] token to concatenate it with the average-pooled patch tokens. We train the linear classifier using stochastic gradient descent (SGD) for 50 epochs. The training data is augmented using random resized cropping. Specifically,
sweep the learning rate over the set (1, 3, 4,5) x 10(-4.-3.-2-11) Importantly, this search is computationally efficient: features from the frozen backbone are computed once per image using a single forward pass and reused across all configurations, since each linear head only requires simple forward pass. For each configuration, we evaluate the classifier on the validation set and report the test accuracy achieved by the best validation configuration. UperNet Probing Evaluation: For UperNet (Xiao et al.] 2018) Probing evaluation, we freeze the pretrained backbone and attach UPerNet decoder head. Specifically, UPerNet decoder and an auxiliary FCNHead. We train only the segmentation heads using the AdamW optimizer for 50 epochs without learning rate warm-up. We conduct grid search over 10
Feature2Pyrami. module the neck, followed by
10 10 10 10-)), and batch size set {16, 32, 64). k-NN Evaluation: assess the quality of the learned representations without any finetuning, we apply aa--parametrc classification using a k-nearest neighbors (k-NN) classifier on the frozen features. In addition to sweeping over E 3, 5, 7, 10, 15, 20, 30, 50, 100 using validation set performance, we follow the same layer selection strategy transformer layers. This protocol does not require additional training or data augmentation, making it lightweight and reliable indicator of raw feature quality in pretrained models. Finetuning Evaluation: For full-model finetuning, we unfreeze the backbone and jointly optimize it with the task-specific head. We perform grid search over learning rates in the evaluation set and batch
base learning rates [10-1,10- 20
linear probing i.e evaluating features from the last
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Table A2:
Scaling behavior of TerraFM models with increasing model size and pretraining data
across four GEO-Bench classification tasks.
Dataset
Model
20%
100%
Gain
EuroSat
TerraFM-S
91.7
92.0
0.3
TerraFM-B
92.0
94.2
2.2
TerraFM-L
92.1
95.1
3.0
BigEarthNet
TerraFM-S
62.6
65.3
2.7
TerraFM-B
63.2
68.7
5.5
TerraFM-LL
62.6
69.4
6.8
So2sat
TerraFM-S 50.5
52.3
1.8
TerraFM-B
49.7
55.1
5.4
TerraFM-L.
49.1
55.9
6.8
Bilc
TerraFM-S
90.5
91.4
0.9
TerraFM-B 91.4
94.5
3.1
TerL
91.0
93.0
2.0
sizes. To stabilize training, we apply reduced learning rate for the backbone, to half of the main learning rate used for the head parameters. Once the best configuration is selected based on validation performance, we evaluate the finetuned model on the test set.
C.2 SCALING TRENDS WITH DATASET SIZE:
We report scaling results on four BenhhBnnch classification tasks when increasing model size and the pretraining dataset from 20% to 100% ATa While all model sizes improve with additional data, the effect is more pronounced for the Base and Large variants. For example, TerraFM-L. achieves 6.8 point gain on BigEarthNet and So2Sat, compared to only 2.7 and 1.8 for TerraFM-SS. On EuroSat and Biln, where performance is already near saturation, the gains are smaller but still positive. These results confirm that larger models are more data-efficient and benefit disproportionately from increased pretraining scale, aligning with scaling laws observed in recent foundation model studies.
C.3 EVALUATION ON REGHLUEEOOUIOO BENCHMARKS.
To further assess generalization, we extend TerraFM's s evaluation to include low-to-high resolution GEO-Bench tasks well as the widely used AID (Xia et al. 2017) dataset (Table A3) Despite being pretrained solely on Sentinel-1 and Sentinel-2, TerraFM achieves consistent improvements over Galileo across diverse sensors and resolutions. Notably, TerraFM transfers effectively to m-forestnet, which uses 15m Landsat-8 inputs compared to TerraFM's 10m Sentinel-2 pretraining resolution, yielding +7.7 point gain from baseline. On fine-scale RGB datasets such m-pv4ger 10.1m) and seahesapeake-landdowee (Im). TerraFM also shows strong gains (+1.5 and +36.8 mIoU, respectively). These results highlight TerraFM's 's robustness across modalities and scales ranging from 0.1-15 m. complementing the evaluations in the main paper.
Table A3: Comparison on low-to high resolution benchmarks.
Datuset
Task
Sensor
Resolution
Galileo
TerralM
m- foresteeee (Irvin et al.
2020
Classification
Landsat 8
15m
49.4
57.1
m-pvdger (Mayer el
2022
Classificution
RGB
0.1m
96.7
98.2
AID (Xia èt 2017
Classification
RGB
78.2
93.8
m-pvager-seg Mayer al. 2022
Segmentation
RGB
0.1m
55.8
85.6
m--hhsaapckk--andee Landooer (Schmiff ct al.
2019
Segmentation
RGB
1.0m
14.6
51.4
m-nz-caitle (Larajji et al. 2020
Segmentation
RGB
0.1m
49.7
68.5
m-NeonTree Weinstein 2020
Segmentation
RGB
0.1m
51.1
54.0
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C.4 EVALUATION ON CHANGE DETECTION.
We evaluate M on the OSCD (Daudt al. 2018) change detection dataset to assess the effect of sensor-invariance temporal tasks (Table A4). Despite being trained without explicit tempo- ral supervision, TerraFM-B with U-Net probing achieves 52.2 mloU, substantially outperforming SSL4BO-S12 Wang et al. 2022) (35.1), SENI2MS (Schmitt et al. 2019) (30.6), and SeCo (Mañas et al. 2021) (28.3). This suggests that TerraFM not only learns robust Pross-senson invariances but also implicitly learns invarinv representations. However, due the nature of DINO loss, which aligns global semantics, the model may still preserve object-level distinctions, resulting in improved performance OSCD.
Table A4: Performance comparison on the change detection.
Method
SeCo
SEN12MS
SSL4EO-S12
TerraFM-B
Fl Score (%) 28.33
30.62
35.08
52.20
D ADDITIONAL ANALYSIS
D.1
QUALITATIVE RESULTS:
illustrates qualitative results for the cloud and cloud shadow segmentation task from Copernicus Beeericuu-BBenc. TerraFM accurately outlines both cloud and shadow regions, effectively distin- guishing visually similar patterns while maintaining spatial coherence across varied scenes.
Figure A2
Image
Label
Prediction
Image
Label
Prediction
Figure A2: Qualitative results for cloud and cloud shadow segmentation. Each triplet shows the input image (left), the ground truth mask (middle), and the TerraFM prediction (right).
D.2 GPU HOUR COMPARISON:
Compared to Prithvi-EO-v2.0, which trains VIT-L (300M) model using up to 80 GPUs for 400 epochs, consuming approximately 21,000 GPU-hours (Szwareman al. (024)) our TerrFMM (300M) achieves comparable scale using significantly fewer resources. Specifically, TerraFM is trained for 200 epochs 64 GPUs, amounting to approximately 12,000 GPU-hours.
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D.3 LANDSLIDE DETECTION
We evaluate landslide segmentation on the Landslide4Sense (L4S) (Ghorbanzadeh et al. 2022) benchmark, which provides segmentation labels for landslide and non-landslide regions across diverse mountainous areas using multi-source satellite data, including Sentinel-2 bands, DEM, and slope information. Our method, TerraFM, achieves strong performance with a mean IoU of 70.8 and a landslide IoU of 43.1, outperforming the Prithvi-EO-2.0 baseline (Table Both TerraFM and Prithvi-EO-22 are trained using focal loss with batch size of 16, Adam optimizer with a learning rate of x 10 Figure A3 shows qualitative results from TerraFM, illustrating predicted landslide masks alongside the ground truth. To assess variability, we repeated the Landslide4Sense experiment with three random seeds and observed stable results: TerraFM-B achieved 70.8 + 0.7 mloU and 43.1 = 0.9 landslide loU.
mIoU
IoU Landslide
Table A5: Landslide detection per- formance on the Landslide4Sense test set. Despite being significantly smaller (120M parameters vs. 300M for Prithvi- EO-2.0), TerraFM achieves higher over- segmentation performance, espe- cially for landslide regions.
Prithvi-EO-2 (300M)
65.0
31.5
TerraFM (120M)
70.8
43.1
D.4 LAND COVER DISTRIBUTION:
Figure A5 illustrates the global spatial coverage of our pretraining data. The selected samples span diverse ecosystems, capturing balanced mix of urban, vegetation, sea, and arid regions. The insets demonstrate fine-grad ene-grained land cover variability, ensuring semantic richness across training tiles. This diverse geographic grounding plays crucial role enabling the generalization capabilities of TerraFM across regions and tasks.
LLM Usage Statement: We used large language models for polishing and improving the clarity of writing, They were not involved in research ideation, experiment design, analysis, or generation of results.
D.5 PSEUDOCODE OF CROSS-ATTENTION FUSION
We summarize the cross-attention fusion mechanism used in TerraFM. The fusion module employs a small, fixed set of shared learnable queries (we use Ng 5) that are applied uniformly across patch size, each modality produces
all spatial positions. For 224 224 crop with 16 N = 196 spatial tokens. At each spatial location n, the modality-specific tokens at that position (M tokens total) serve as input to keys and values computation, while the shared queries attend over these modality tokens to produce N, intermediate outputs. These per-location outputs are then aggregated into single fused token, yielding fused sequence of length N (plus the class token) for the ViT encoder. Thus, the fusion block preserves the backbone's 's original sequence length and performs modality mixing independently at each spatial location, with spatial interactions handled by subsequent transformer layers. Below, we provide concise PyTorch-style pseudocode implementation of this per-location fusion mechanism.
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def cross_attent ion_ftttettio_fiuiinn (2_al1, q, w_q, w_k, w_v, P_r, mha):
alall IN, M, DJ
tekens at N spatial positions, M modalities shared learnable queries (reused for all
IN [
positiens)
projection for scoring aggregated query outputs N, M, D 2_all.shar
p_r [D, 1]
N_ shape
Project shared queries once (reused across all spatial positions) Q = (g) # [N_9, D]
fusea_tokens = 1]
for n in range (N)
# Tokens from all modalities at spatial position # [M, D]
x_n [_all[n]
Linear projections keys and values
K_n R_k(x_n)
# IM, DJ
V_ R_v(x_n)
# [M, DJ
Cross-attention over medalities at position
(2 attends to K,, V_n; returns N_g outputs) z >_prime_n, mha (Q, K_n, V_n) [N_, D]
Learned weighted mean ever N_q query outputs scores (z_prime_n) @ p_r). s-quezzee $ [N_q]
w scores.softmax. (dim=0)
[N_q]
z fused (wl:, Nonel * 2 prime n) sum (dim=0)
[D]
fused_tokens. append (a_ f=see___n)
Final fused sequence fed to the shared encoder Enc_phi 2 fused = torch. stack (fused_tckens, dim=0) $ [N, D]
return 2 fused
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Image
Ground Truth
TerraFM
Figure A3: Qualitative results for landslide segmentation. Each triplet shows the input image (left),
the ground truth mask (middle), and the TerraFM prediction (right).
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Figure A4: Global spatial distribution of the Major-TOrr TOM (Francis & Czerkawski 2024) training subset. Each square shows 1 x 1 cell, colored by the number of 10.68 km x 10.68 km tiles it contains.
GridCells Majority LandCover
Bultuup
Herbapoous Weslond
Open Beas
Snow and Ice
Gropland
Mangroves
Permonent Water Bodies
Tree Cover
Bare Sperse Vegetation
Grassland
Moss and Lishen
Shrupland
Figure A5: Global distribution of sampled training tiles by dominant land cover class, based on ESA WorldCover labels. Insets show detailed tile-level diversity, highlighting coverage across built-up. vegetation, and water classes.
23


Reviews:
[{"review_id": "b7g7DMQEQa", "reviewer": "Reviewer_omAQ", "summary": "This paper presents TerraFM, a self-supervised remote sensing foundation model trained with Sentinel-2 and Sentinel-1 data. Compared to other remote sensing FMs, TerraFM has a much larger spatial input size. The model is trained using contrastive learning with different modalities used to construct augmentation pairs. The modalities are encoded separately and fused with cross-attention. The paper introduces a novel dual centering strategy that adds an additional prior about LULC distributions beyond the DINO centering strategy. Experiments on  Geo-Bench and Copernicus-Bench show that TerraFM outperforms most other models across most tasks.", "questions": "- What are the differences between the paper’s contributions and the works I referenced in the Weaknesses question?\n- Were the experiments repeated for different random seeds and/or subsets of the test data to quantify the standard error of performance metrics?\n- What are the differences between the evaluation protocols for the results taken from the Galileo paper and those applied for TerraFM?\n- Why was the segmentation protocol changed from linear probe to upernet probe for the ablation experiment?\n- WorldCover is known to have highly variable accuracy across the world, with especially poor performance in Africa (see [Kerner et al., 2024](https://www.nature.com/articles/s41597-024-03306-z) for one analysis). Since this is used as a prior in the dual centering method, do you expect performance to be worse in regions where WorldCover has worse performance?\n- Can the authors add rows to the ablations table that isolate the contribution of each component?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The paper is well-written and well-organized. It is easy to read and follow the technical details.\n- The paper presents extensive experiments on Geo-Bench, Copernicus-Bench, and other datasets in the appendix. The ablation experiments test the contribution of each component.\n- The performance is good across all tasks, with similar magnitudes across GeoBench tasks. This is particularly evident in Figure 1.", "weaknesses": "- The paper claims several novel contributions to the FM design: modality-specific patch embedding, modalities as augmentations, cross-attention fusion block, and dual-centering strategy. I think the dual-centering strategy is new (although very similar to the DINO centering), but it seems that all of the others have been proposed in prior work. The paper does not discuss how their contributions compare to these prior works.\n    - modality-specific patch embedding: This is a common strategy in remote sensing FMs today. Maybe the authors realize this since it was not part of the ablation. I don’t think it can be claimed as a contribution. For example, SatMAE, Galileo, RingMo, and more all use separate embedding layers/tokenizers for each modality (or groups of channels within modalities). This was also done in vision with [MultiMAE](https://arxiv.org/pdf/2204.01678).\n    - modalities as augmentations: The idea of using colocated pairs of images from different modalities as augmentation pairs for contrastive learning is also well established in prior work (e.g., [Jain et al., 2022](https://arxiv.org/abs/2209.02329); [Prexl & Schmitt 2023](https://openaccess.thecvf.com/content/CVPR2023W/EarthVision/papers/Prexl_Multi-Modal_Multi-Objective_Contrastive_Learning_for_Sentinel-12_Imagery_CVPRW_2023_paper.pdf); and CROMA).\n    - cross-attention fusion block: I believe the same thing was done in [CrossMAE](https://openaccess.thecvf.com/content/CVPR2024/papers/Guo_CrossMAE_Cross-Modality_Masked_Autoencoders_for_Region-Aware_Audio-Visual_Pre-Training_CVPR_2024_paper.pdf) and other remote sensing specific work like [Chan-To-Hing & Veeravalli 2024](https://arxiv.org/abs/2401.02764).\n- There is no discussion of performance variability and stability across runs/seeds or samples. Were the experiments repeated for different random seeds and/or subsets of the test data to quantify the standard error of performance metrics?\n- I have some questions about comparisons between TerraFM and previous models (taken from Galileo paper) on downstream tasks:\n    - Tseng et al. 2025 (Section C) describes the hyperparameter sweep done for evaluating downstream tasks. From the TerraFM paper’s Section C with evaluation implementation details, it seems there are similarities but not the same protocols compared to Galileo. I would expect TerraFM to use the same protocol as Galileo if they are trying to compare directly to numbers from Galileo’s tables.\n    - The paper says the authors used linear probing, UperNet probing, k-NN, and fine-tuning for downstream task evaluation. It seems that all segmentation tasks except the ablation analysis used linear probing, but the ablation used UperNet probing for cashew plant. Why was the protocol changed here? Why was the cashew plant task chosen here? From my experience the labels in this dataset are noisier and lower resolution (and very sensitive to scale) compared to other geobench tasks.\n- Some formatting and writing issues\n    - The margins are smaller than allowed. This gives the paper a lot more space than other authors have to work with on their submitted papers. I expected this to constitute a desk reject.\n    - The in-text citations need to be changed to use \\citet (bracketed)\n    - The style of Table 4 is notably different than the prevoius tables\n- Minor issues\n    - Table 1 has several confusing or unexplained details: I was confused that “Scale” represents the number of training samples not the number of model parameters. I would also have expected to find the # parameters in this table. Why is Benchmarks a relevant column here? How is Pixels (~T) computed?\n    - There are some missing or unclear details about the pretraining data. The paper says “global distributional priors” were used - what priors/where did these come from? The paper says they “enriced each grid cell with metadata” - what metadata exactly and how was this formatted/interpreted by the model?\n    - The “Impact of Components” paragraph discusses gain % for individual components, but those components are not isolated. For example, the 14% gain for cashew plant is from all of the components added, not just dual centering. Can the authors add rows to this table that isolate the contribution of each component?\n    - The caption of Figure 6 talks about GeoBench as a whole, but the results are only shown for m-EuroSat.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "nTX4nCOwHw", "reviewer": "Reviewer_YoBk", "summary": "The authors introduce TerraFM, a S1/S2 foundation model for earth observation (EO) data. The paper proposes a modality-specific self-supervised pre-training recipe aimed specifically at EO domain-specific data characteristics. The authors incorporate a custom patch embedding mechanism catering to the multispectral nature of the data, treat different modalities as separate views for a multiview-learning setting, introduce an attention-based fusion mechanism to incorporate modalities within the model, and propose a dual-centering mechanism to handle the skewness of the underlying data distribution. Experiments indicate that the model robustly outperforms comparable foundation model efforts on two extensive benchmarking datasets.", "questions": "- An complementary alternative explored to the dual-centering done to encourage learning long-tailed features of the data is altering dataset curation. While data sampling in this work is done based on land cover priors, have the authors considered replicating efforts like dynamic dataset curation (arXiv:2504.06962) instead or in tandem with the dual-centering strategy explored here to see how that impacts model performance?\n- Similarly, since resolution varies between S1 and S2 imagery and acquisition modes, have the authors considered further augmenting the ViT backbone with domain-specific mechanisms, such as the scale information incorporated by ScaleMAE (arXiv:2212.14532)?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- This paper has strong contributions to the literature, proposing a self-supervised pretraining strategy based on several well-motivated, domain-specific architecture components, including the double-centering strategy which is novel. \n- The experiments are comprehensive and indicate strong performance, especially when including the supplement, building confidence in the quality of the model.", "weaknesses": "- The paper exceeds length requirements and ignores formatting guidelines (e.g. margins), this has to be fixed. \n- The authors do not justify the choice not to incorporate any other augmentions in the pre-training framework. Many successful works  in the EO literature have previously done joint-embedding learning based on heavier augmentation strategies that are absent here, is there a reason?\n- The main text (and appendix) lacks any details on model tuning or justification for hyperparameter choices.\n- Table 2 and 3 use underlining to highlight second best models, this should be clear from the table description. Further, Table 4 does not use the same highlighting but should at least be consistent. \n- Table 5 should show improvement from baseline SS performance for subsequent rows instead of the absolute scores, this would make it much more readable.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "gbDNi082rq", "reviewer": "Reviewer_Npar", "summary": "The paper introduces TerraFM, a large-scale self-supervised foundation model for Earth Observation (EO) that unifies multi-sensor data—specifically Sentinel-1 SAR and Sentinel-2 optical imagery (L1C, L2A)—under a common framework. TerraFM is built on a student-teacher contrastive learning framework (similar to DINO) and introduces three main technical contributions to address challenges specific to EO data.The authors conduct extensive experiments on the GEO-Bench and Copernicus-Bench benchmarks. The results show that TerraFM consistently outperforms prior state-of-the-art models in both classification and segmentation tasks", "questions": "1. How does the fusion module handle missing modalities at inference time? For example, if Sentinel-1 or Sentinel-2 data are unavailable, is there a fallback strategy?\n2. Can the proposed modality-specific patch embedding extend easily to more than three modalities (e.g., DEM, hyperspectral data)?\n3. Does TerraFM consider temporal alignment between Sentinel acquisitions, or is it purely spatial? If not, how might the framework extend to temporal FMs?\n4. How sensitive is the model to the choice of the α parameter (balancing global and frequency-aware centers)? Was this tuned per dataset?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Well-written and organized; clear motivation and architectural diagrams.\n2. Strong empirical results: Their method outperforms all baselines on GEO-Bench and Copernicus-Bench for both classification and segmentation tasks.\n3. Clear ablation studies isolate the impact of each component—modality augmentation, fusion, and dual centering—showing substantial performance gains", "weaknesses": "1. Although Major-TOM sampling is described, the paper gives limited discussion of potential geographic or seasonal biases in the curated dataset and how these might affect global transferability.\n2. The model is designed for three specific inputs: S1, S2-L1C, and S2-L2A. While it generalizes well (Appendix C.3), the paper doesn't discuss the practical steps needed to extend TerraFM to a new modality (e.g., Landsat, hyperspectral).\n3. The paper margin is smaller than the official format provided.\n4. The cross-attention fusion module is activated \"When multiple modalities M ⊆ M are selected\" (L235-236). Given the stochastic assignment to student and teacher networks, this needs clarification. Does this mean fusion occurs within the student network if it happens to be assigned both S1 and S2 while the teacher does not use fusion?\n5. The paper claims an advantage from using \"large spatial tiles\" (534px) to capture broader spatial context. However, the model's actual input resolutions are standard (224×224 for global crops, 96×96 for local crops). it is unclear to a reader that how much of the performance gain comes from this cropping strategy.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "fUTV0SkWmg", "reviewer": "Reviewer_Zhcb", "summary": "The paper introduces TerraFM, which is a foundation model for remote sensing / earth observation. TerraFM leverages the following novel strategies: modality-specific patch embedding, natural augmentations, cross-attention fusion, and class-frequency-aware centering (in contrastive learning). TerraFM can process Sentinel-2 images (multispectral optical), Sentinel-1 images (synthetic aperture radar), or both. TerraFM achieves SOTA results across standard remote-sensing benchmarks.", "questions": "- Please clarify the cross-attention fusion\n- In Table 5, centering is ablated. Does this mean that both centering terms are removed together? If so, how can we know the effect of the novel centering term introduced?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 1, "contribution": 2, "strengths": "- TerraFM achieves SOTA performance across standard RS benchmarks and over standard RS baseline methods\n- The paper is, in general, clearly written and presented\n- The cross-attention token-fusion strategy seems novel (although is unclear, see weaknesses)\n- The class-frequency-aware centering strategy is novel to the best of my knowledge", "weaknesses": "- The novelty of the proposed strategies is often overstated and sometimes incorrect, in my view. For example, \"modality-specific patch embedding\" is just a different patch embedding layer per modality, which is commonly used (e.g., Galileo or even SatMAE). Treating sensor modalities as natural augmentations for contrastive learning is also not novel, e.g., CROMA performs radar-optical contrastive learning. Thus, the cross-attention fusion block and centering strategies are the only methods that I consider novel to TerraFM.\n- The cross-attention fusion block is unclear to me. My understanding is that the images are patchified according to their modality, which become keys/values in cross-attention with learnable queries. However the number of learnable queries is unclear, as this determines the sequence length for the transformer backbone, this is crucial. In the appendix, it implies that there is 1 query per spatial location. Does this mean that with two modalities (2x196 patches), there are 196 learnable queries attending to 392 patches? The bottom of section 3.1 implies that each query only attends to the patches at the same spatial location. Can you please provide code to make this clear for me?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)