PhySwin: An Efficient and Physically-Informed Foundation Model for Multispectral Earth Observation
Authors: Chong Tang
Joseph Powell
Dirk Koch
Robert D. Mullins
Alex S. Weddell
Jagmohan Chauhan
Venue: NeurIPS 2025 poster ()
Keywords: Foundation Model
Physics-Informed ML
Efficient Machine Learning
Multispectral Image
Remote Sensing
Primary area: deep_learning

Abstract: Recent progress on Remote Sensing Foundation Models (RSFMs) aims toward universal representations for Earth observation imagery. However, current efforts often scale up in size significantly without addressing efficiency constraints critical for real-world applications (e.g., onboard processing, rapid disaster response) or treat multispectral (MS) data as generic imagery, overlooking valuable physical priors. We introduce PhySwin, a foundation model for MS data that integrates physical priors with computational efficiency. PhySwin combines three innovations: (i) physics-informed pretraining objectives leveraging radiometric constraints to enhance feature learning; (ii) an efficient MixMAE formulation tailored to SwinV2 for low-FLOP, scalable pretraining; and (iii) token-efficient spectral embedding to retain spectral detail without increasing token counts. Pretrained on over 1M Sentinel-2 tiles, PhySwin achieves SOTA results (+1.32\% mIoU segmentation, +0.80\% F1 change detection) while reducing inference latency by up to 14.4$\times$ and computational complexity by up to 43.6$\times$ compared to ViT-based RSFMs.

PhySwin: An Efficient and Physically-Iffrrmed Foundation Model for Multispectral Earth Observation
Chong Tang University of Southampton UCL AI Centre Southampton, United Kingdom chong. tang@soton. ac. uk
Joseph Powell University of Manchester Manchester, United Kingdom j woseph.powelleeeeoo Cmanchester Comceeeneeeeeeeeeeeeeeer ac uk
Dirk Koch University of Manchester Manchester, United Kingdom dirk. koch@manchester. ac.
Robert Mullins
University of Cambridge Cambridge, United Kingdom robert. interr.muulinseee cam ac.
Alex Weddell University of Southampton Southampton, United Kingdom aswθecs soton. ac uk
Jagmohan Chauhan
University College London
UCL AI Centre
London, United Kingdom
aagmohan.chaubannuul....
chauhan@ucl ac.
Abstract
Recent progress on Remote Sensing Foundation Models (RSFMs) aims toward universal representations for Earth observation imagery. However, current efforts often scale up size significantly without addressing efficiency constraints critical for real-world applications (e.g., onboard processing, rapid disaster response) treat multispectral (MS) data as generic imagery, overlooking valuable physical priors. We introduce PhySwin, foundation model for MS data that integrates physical priors with computational efficiency. PhySwin combines three innovations: (i) physics-informed pretraining objectives leveraging radiometric constraints to enhance feature learning; (ii) an efficient MixMAE formulation tailored SwinV2 for low-FLOP, scalable pretraining; and (iii) token oofen-efficient spectral embedding to retain spectral detail without increasing token counts. Pretrained on over 1M Sentinel-2 tiles, PhySwin achieves SOTA results (+1.32% mloU segmentation, +0.80% Fl change detection) while reducing inference latency by up to 14.4x and computational complexity by up 43.6x compared ViT-based RSFMMs.
Introduction
Earth-observation (EO) programmes now deliver petabyte-scale streams of multispectral (MS) im- agery global coverage and daily revisit rates (e.g.. Sentinel-2 (S2), Landsat-8) [Roy al. 2014 Drusch et al. 2012). Such data supports applications ranging from precision agriculture and biodiver- sity monitoring to rapid flood and wildfire assessment [Gorelick et al., 2017 Van Etten et al. 2018 Effectively utilizing the data requires models that produce general representations, enable rapid task adaptation (fine-tuning) and ensure computational efficiency for deployment.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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@SSkSSneee
PhySwin-8
Frequency (x10*)
Absolute Differance
0.03
OSCD Score (%)
HyperSSIMA
PhySwin-T
X0
Parameters (milllonss)
125
1000
SpectralGPT
0.04
SatMAE++ SalMAE
CaCo
73
20
SeCo
80
850
86000
Reflectance Value
Band Transition
Inference Throughput (images/s)
Figure I: Motivations for PhySwin. (a) Bounded reflectance distribution in Sentinel-2 multispectral data, illustrating the conggycoonservation property that motivates our energy-bound loss. (b) Smooth transitions across adjacent spectral bands, motivating the spectral-smoothnes constraint used in physics-informe pretraining. (c) PhySwin's 's balanced performance among EO foundation models.
trade-off in change detection, highlighting
Following the scaling trends in vision and NLP, Remote Sensing Foundation Models (RSFMs) have evolved from ResNet-based contrastive learners [Manas et al., 202 Mall et al. 2023 to Vision
Transformer (VIT) backbones with masked autoencoding (MAE) Hong et al. 2024 Cong et al.
2022 Reed et al., 2023 Subsequent models further increase capacity by fusing modalities or scaling to ballion-parametto encoders [Guo et al.] 2024 Wang et al. 2025a improving accuracy but sharply raising computational costs. This has driven interest in efficiency-oriented backbones. State-space models (SSMs) adapted for EO [Wang et al., 2025b Chen et al., [024]] offer an efficient parametric alternative, while hierarchical VIT's like Swin Transformer [Liu et 2002 2022a] reduce inference complexity. Other designs address redundancy, such as HyperSIGMAY A's Sparse Sampling Attention [Wang al., 2025a]. . However, we argue that architectural optimizations alone cannot fully
meet the dual goals of high accuracy and deployment-level efficiency required by EO systems.
Beyond computational efficiency, RSFMs often overlook the rich physics in MS data but treat them as generie image channels. Each MS band captures physically mcaningful surface reflectance shaped by radiative-transfer processes [van Trigt 1990 Hapke prior knowledge beyond image statistics. As shown in Fig. Ia and b, Sentinel-2 reflectance values and band transitions exhibit bounded and smooth trends, which can guide model training and promote more informative feature learning. So far, physics-aware learning has improved EO applications, for example, in crop-nitrogen retrieval (Dehghan-Shoar al. 2024] and solar-irradiance forecasting (Liu et al. 20226), outperforming data-driven baselines. Such priors promote faster convergence, better generalization and more informative representations. Despite this potential, most current FMs ignore these physical priors, prioritizing computational or generic vision approaches. Thus, a critical gap remains: developing models that fuse physics-awas awareness with computational efficiency for powerful and practical EO deployment under operational constraints.
1981 Tominaga and Wandell T990]. offering
We introduce PhySwin, a foundation model designed for MS imagery that integrates physical priors with computational efficiency through three complementary innovations: First, novel pretraining objectives embed radiometric constraints, including adjacent-band spectral smoothness and energy conservation, into self-supervised learning (SSL). This leads to more robust and meaningful fea- ture representations. Second, PhySwin refines the Mixed and Masked Autoencoding (MixMAE)
method [Liu et al. 2023 to address the limitations of standard MAE and computationally intensive MIM variants in hierarchical models. Our approach leverages SwinV2's s shifted window mechanism for scalable, low-cost pretraining while enabling effective cross-window interaction for global context modeling. Third, PhySwin embeds MS data efficiently by grouping spectral bands and concatenating distinct feature subspaces per patch to retain spectral detail without increasing token counts. During pretraining. spectral group masking randomly removes entire groups of features to simulate spectral variability and enhance robustness. Pretrained on over one million S2 tiles and evaluated on six EO benchmarks, Swyn outperforms strong ViT baselines, improving segmentation mIoU by +1.32% and change detection Fl by +0.80% while reducing inference latency and computational cost by up to 1 14.4x and 43.6x, respectively. As shown in Fig. [TC. PhySwin achieves a favorable balance compared against other state-of-thed oo-tte--rttttttt (SOTA) basclines. Our main technical contributions are:

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The first integration of physics-aware objectives into scrge-scale MS foundation model pretraining, using physical constraints to generate high-fidelity features that overcome the typical accuracy trade-offs associated with computationally efficient architectures.
A novel MixMAE formulation for SwinV2 that enables highly efficient and resolution- flexible pretraining on large EO datasets.
A token-efficient spectral embedding technique that efficiently represents comprehensive MS band information without increasing token counts, addressing a common efficiency issue in existing RSFMs.
2
Related Work
Backbone Evolution and Pretraining Paradigms. Early EO FMs adapted contrastive learning on
CNNs to leverage satellite time-series consistency (Manas et al. 202 Mall et al. 2023]] Subsequent
work adopted Transformer backbones, particularly as MAE techniques proved effective for large-scale
pretraining (Cong et al. 2022]. This shift stimulated architectural diversification from standard ViTs
compatible with basic MAE to hierarchical Swin-style transformers [Guo al. [024] requiring
adapted pretraining, and to sfficiency-focuseee state-space models [Chen et | Wang et al.
20256). Additional innovations include MAE variants tailored for scale [Noman et al. 2024 Reed
et al. 2023] or spectral data (Hong& et al.) (2024), multi-tasking [Wang et al.] 2024] continual learning [Mendieta et al., 2023 and multi-modal fusion [Guo al. | Nedungadi et al. 2024].
While significant progress has been driven by scaling models and datasets to enhance performance, recent research has increasingly prioritized computational efficiency and scalability for practical deployment, particularly resource-constrainee platforms [Chen al. 2025 Wang al. 2025b
Efficient Foundation Models. FM architectural choices strongly influence computational efficiency. Hierarchical transformers like Swin [Liu et al. [022a] reduce complexity from quadratic to linear
compared to standard ViTs through windowed attention. SSMs [Gu and Dao [023] offer linear scaling for long sequences, though applicability may depend on task-specific adaptations or pretrain- ing strategies. These designs often complicate pretraining. For example, MAE's patch discarding
conflicts with the structure of hierarchical models [Liu et al.) 2023] A common alternative uses
MIM with [MASK] tokens (e.g., SimMIM [Xie et al. 2022]), though these non-informative tokens add inefficiency and pretrain-finetun, discrepancies. Other approaches include supervised pretrain- ing [Bastani et al. 2023] and contrastive learning, but MAE's reconstruction objective often produces
richer features for dense prediction tasks (He al., 2022 while avoiding the label demands of supervised methods. Returning to our focus on EO tasks, the existing work on FM architectures and pretraining methods inspired our realization that designing efficient RSFMs requires balancing architectural benefits, pretraining complexity, and performance trade-offs.
mrysics-informed MI. Incorporating domain knowledge enhances foundation models. Physics- informed learning, which integrates physical laws as priors, has shown promise climate modeling, materials science, and manufacturing [Karniadakis al. 2021 Dehghan-Shoar et al. [2024.. Applying such priors in large-scale RSFM pretraining remains underexplored. MS imagery well-suited due to physical principles governing surface reflectance: smooth spectral variation across adjacent bands (van Trigt 1990 Tominaga and Wandell] 1990] and energy conservation bounding reflectance between 0 and Hapke 1981]. Leveraging these priors during pretraining improves representational power and efficiency. PhySwin follows this principle, combining physics-infone informed objectives with efficient architectures for high accuracy and practical deployment.
3 PhySwin
We identify three core challenges in building efficient RSFMs: (i) pretraining inefficiencies of hierarchical architectures; (ii) the explosion of token counts when naively handling MS data; and (iii) the degradation of representation quality under tight compute budgets. To address these challenges, we propose PhySwin, an efficiency-orrented RSFM that leverages refined MixMAE pretraining, token-efficient embedding and physies-aware objectives to achieve high performance on EO tasks with significantly reduced computational cost (Fig 2) detailed in the following subsections.
3

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Group Bands
Efficient
Physics-Aware
MixMAE Pretraining
Applications
AC BB.vounc
Downstreem Heads (UperNatFON:
Cree
Swinn2
Maxing
Leear Projecion
Block
Patch Merging
Merging
Mask
Tokens
Slage 2
altch Merq
Stapee
Stage
Limnte
Decoder
Dual
Petch
Reconstructin
Poe Embedd
Stage
Token-Efficient
Spectral Cube
Embedding Masking
Two MS cubes are mixed via spatial masking, then grouped by band types and embedded. The SwinV2 encoder with SW-Masking processes the inputs through hierarchical stages. Pretraining is guided by Leee and regularization: Lamoath and Lhound- PhySwin achieves high performance across diverse downstream tasks with improved efficiency.
Figure 2:
PhySwin framework:
3.1 Physically-Informed Pretraining Objective
Multispectral sensors (c.g., S2, Landsat-8) measure surface reflectance across bands, each encoding distinct material properties (e.g., vegetation health in Near-Infrared (NIR), moisture in Short- Wave Infrared (SWIR) spectroscopy). We embed two radiometric priors as regularizers on the reconstructed reflectance vector i .7.....,f2 ":
Spectral Smoothness. Natural surface reflectance spectra are usually smooth function of wave- length, lacking sharp or random fluctuations between adjacent bands. This well-established property stems from the continuous nature of light interactions with materials and is supported by both theo- retical analysis [van Trigt [990] and empirical studies [Tominaga and Wandell] [990] (as shown in Fig. [p). To discourage fitting noise or sensor artifacts, we impose a smoothness regularization based on the first-order finite difference:
B-1
smooth
Σ (fo+1 16)²
(1)
which penalizes high-frequency spectral fluctuations.
Energy Conservation. Physically, surface reflectance quantifies the fraction incident electromag- netic energy reflected at each wavelength. Due to the energy conservation, this value is inherently bounded, typically within the range [0, 1 (Hapke 1981] (seen in Fig. (b). Incorporating this constraint guides the model operate within physically realistic output space. To enforce this constraint, apply:
Dbound (ReLU(-A)) + ReLU(fi 1.2)].
(2)
Both terms remain fully differentiable. For implementation, we relax the upper bound to 1.2 to accommodate sensor noise.
3.2 Efficient Pretraining via Refined MixMAE on Swin
PhySwin adopts Swin Transformer V2 [Liu al. 2022a| for its computational efficiency: the Swin family scales linearly with input size via the shifted window self-attention (SW-MSA). Swin V2 extends VI with improved scaling and log-spaced continuous position bias [Liu et al. 2021], which enhances transferability across resolutions and window sizes and is particularly useful for the diverse spatial scales of EO.
As discussed in Section 2 efficiently pretraining hierarchical ViTs remains nontrivial. To address this bottleneck, MixMAE [Liu al. 2023 proposed. Its core idea is to replace masked patches from one image (x.²) with visible patches from a second image (x.") using random binary mask M. The resulting mixed input contains only real image tokens (seen the mixing process in Fig. 2
x T M + + -M).

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A lightweight decoder reconstructs both original images under dual reconstruction loss:
Lree = 11 - o (1 - M)1B + 11 x²)) @ M
(3)
where of and y/ are the reconstructions of æj and u.y, respectively. This dual reconstruction objective ensures that each source image is reconstructed solely from its own unmasked context, encouraging the model infer missing regions using within-source evidence rather than cross-source tokens. During encoding. tokens attend only to others from the same source image, dictated by M, preventing information leakage. This design enables MAE-style pretraining to be applied to hicrarchical ViTs while avoiding non-informative [MASK] tokens in the encoder. Notably, the original MixMAE implementation disables SW-MSA and instead relies on large fixed windows to model global context.
PhySwin refines MixMAE for V2 by retaining the SW-MSA mechanism to support improved cross-window interactions and global context modeling (seen in S2in block in Fig. 2). Specifically, we introduce SW-Masking, in which the binary mask M is spatially shifted in alignment with SW- MSA configurations. We argue that coordinating the masking pattern with shifted windows promotes more robust feature learning across window boundaries, preserves spatial continuity, and reduces reconstruction artifacts compared to Exed-window schemes.
3.3 Token-Efffcient Grouped Spectral Embedding and Masking
Standard ViTs embed non-overlapped RGB patches (R (R (xxxxx,, x3) into D-dimensional tokens, pro- ducing N tokens for N patches. Directly extending this to MS data xR# with > 3) may diminish critical band-specific information. Prior works address this via methods like grouped channel embeddings [Cong et al.) [002]] 3D spatial-spectral tokens [Hong et al.] 2024] or separate feature branches [Wang et al., 2025a]. However, these techniques increase token counts and may overlook correlations between spectral bands.
We propose token-efficient embedding strategy that preserves the token count comparable to standard processing while retaining the rich spectral structure of MS data. Given sample x E # Wx x we partition the B spectral bands into G' physically coherent groups (e.g., visible, NIR, SWIR ) .r9) E IH Hx with Σ Dg D. For each spatial position (i,j), we extract group-wise
local patches 2. E PxPxB, Each group g is then processed by lightweight, group-specific embedding function fg RxPxx + RR,,, and the final token eij E RRD constructed by concatenating the outputs across all groups (Fig, 2
eij = Concat (f(o= f2(4.))......... fo(o!9)) E IR..
(4)
This yields single embedding vector Ct, IR. per spatial location. Therefore, an input image divided into N spatial patches produces exactly N tokens, maintaining the sequence length efficiency.
Building on this design, we further introduce Spectral Group Masking (MaskSpec) during pretraining to improve representation robustness and simulate real-world spectral variations. PhySwin randomly
zeros out or spectral group subspaces fg (=([)) within each token Ci.j. encouraging repre- sentations that are less reliant on any single spectral group. This spectral masking complements the spatial masking of our refined MixMAE framework (Section 3.2), where we a fixed 50% mixing ratio. By operating in the spectral domain, we can modulate the overall pretraining difficulty. To summarize the combined masking strategy, for each token Ci.j. we apply:
ر MixMAE MMakSSpec ( M()) MaskSpec (0) M(2), M),
(5)
where Mf41) E 13. are independent group masks. This joint masking regulates spatial and spectral exposure, yielding more generalizable features.
3.4 Pretraining Details
Datasets and Preprocessing. Following a two-stage pretraining strategy similar to Spectral- GPT [Hong et al. 2024]. PhySwin is first trained for 200 epochs on FMoW-S2 (about 712, 000 samples) [Christie al. [2018] with 96 x 96 $2 tiles, then for 100 epochs on BigFarthNet-S2 (about 590, 000 samples) (Sumbul et al. 2019 with 128 x 128 tiles. Raw reflectance values normalized
5

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by 1/10,000 to approximate the [0. 1] range. We exclude three 60 meteet resolution S2 bands (B01, B09, B10) following SpectralGPT and SatMAE [Hong et al., 2024 Cong al. 2022]. The retained 10 bands are grouped into: Visible (B02, B03, B04), Red-Edge/Nin (B05, B06, B07, B08, B8A), and SWIR (B11, B12) corresponding to our embedding method (Section 3.3
Backbone Configurations. PhySwin is developed using two Swin Transformer V2 variants, both configured with base input resolution of 128 128, patch size of 4 and a window size of 7.
Variant
Embedding Dim
Depths
Num Heads
#Params (M)
PhySwin-Tiny (T)
96
[2, 6, 2] [2, 2, 18, 2]
[3, 6, 12, 24]
29
PhySwin-Base (B)
128
[4,8, 8, 16, 32]
88
Training Setup. PhySwin was pretrained on eight NVIDIA RTX 8000 GPUs using mixed precision. We employed the AdamW optimizer with a cosine learning rate schedule and a 15-epoch linear warmup. The training objective
Liotal = Lrec + XCsmooth + 3.uudd
(6)
Weights were fixed during pretraining at À 0.25, 3 = 0.1. Full hyperparameters are detailed in Appendix A.
4 Experiment
In this section, we evaluate the effectiveness and efficiency of PhySwin against SOTA RSFMs across four downstream EO tasks. To ensure reproducibility, PhySwin is built on the Hugging Face Transformers library [Wolf et al. 2020), following its coding standards and API conventions. PhySwin models and SOTA baselines replicated in this study fine-tuned under unified protocol, including SeCo Nees,e CACo (ResNet50), SatMAE (ViT-B), SatMAE++ (ViT-L) and SpectralGPT (ViT- B). Performance for other baselines (e.g.. SkySense, HyperSIGMA) is reported from the literature. Across all tasks, PhySwin uses the following S2 bands: native (B02, B03, BO4, B08) and resampled 10m (B05, B06, B07, B8A, B11, B12). Extended experimental results, including additional benchmarks and ablation studies, are provided in Appendix
4.1 Benchmarks
Semantic Segmentation. Performance is evaluated on two benchmarks. SegMunich (Hong et al. 2024] contains overlapping 128 x 128 pixel tiles (50% overlap) with 13 Land Use and Land Cover (LULC) classes. SynaamieEattNNet-Setinne
(Syna.-S2) [Toker al. 2022 consists of monthly $2
composites from January 2018 to December 2019, tiled into 256 x 256 patches. Mean Intersection over Union (mloU) is reported for both.
Change Detection. Evaluated on OSCD [Daudt et al. 2018), comprising 24 S2 image pairs (14 train, 10 test), split into non-overlapping 96 x 96 patches as the SkySense protocol [Guo et al.) 2024] Precision, Recall, and Fl are reported. Dyna.-S2 image pairs are formed from monthly composites, tiled into 96 96 patches, and semantic change segmentation score (SCS) Toker et al. 2022 is calculated from 7-class segmentation labels.
Scene and Multi-Label Land Cover Classification. Scene classification is evaluated on two benchmarks. FMoW S2 contains 62 land-use types, we follow the SatMAE splits [Cong al. 2022
and report top-1 accuracy. EuroSAT (Helber et al. [018 2019 comprises 27, 000 S2 images (64 x 64 pixels) across 10 land cover classes; we follow [Helber et al. 2019) and report overall accuracy (OA).
Multi-label classification is evaluated on Big which contains 120 x 120 $2 images from 10
countries annotated with 19 land cover classes, using official splits CCaasnn et al. 2024]. We report mean average precision (mAP).

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Table Semantic segmentation performance
Model
SegMunich
AmloU (%)
Dyna.-S2
AmloU (%)
SeCo
45.92
-5.08
40.19
-6.01
CACo
44.87
-6.13
41,50
-4700
SatMAE
48.71
-2.29
38.73
-7.47
SatMAE++
50.62
-0.38
42.87
-3.33
SpectralGPT
51.00
44.72
-1.48
SkySense!
46.20
PhySwin-T
49.53
-1.47
43.80
-2.40
PhySwin-B
52.32
+1.32
46.53
+0.33
Pre-Change
Post-Change
Ground-Truin
CACo
Spectra/GPT SatMAE++
PhySwin-T
PhySwin-B
ROI
Ground- Truth!
SeCo
SatMAE++
SpectralGPT
PhySwin-T
PhySwin-B
SegMunich
Dyna.SS -22
Figure 3: Performance visualization of downstream tasks. TOP: OSCD change detection. BOTTOM:
SegMunich and Syna.S2 semantic segmentation. Colors follow each dataset's official palette.
4.2 Downstream Tasks Performance
Semantic Segmentation Results. Table I mloU on SegMunich and Dyna.-S2 compared to RSFM baselines. On SegMunich, PhySwin-T achieves 49.53%, ranking third behind SpectralGPT (51.00%) and SatMA++ (50.62%) and surpassing SatMAE ViT-B (48.71%). On Dyna.-S2, it reaches 43.80% outperforming both SatMAE variants while using only one-third the tokens of SpectralGPT. PhySwin-B sets new SOTA results with 52.32% (+1.32% over SpectralGPT) on SegMunich and 46.53% (+0.33% over SkySense) on Dyna.-S2. These results show PhySwin improves dense prediction accuracy without increasing model size runtime. Qualitatively (Fig. 3 PhySSwn produces sharper boundaries and better recovers thin structures, such as narrow roads and small bodies, matching the quantitative improvements in Table
Change Detection Results. Table reports F1, precision, and recall on OSCD and SCS on Dyna.-S2. PhySwin-T achieves 58. 74% F1, outperforming ChangeMamba (57.20%) and all ViT-based methods, and records 17.61 SCS, closely matching SkySense (18.00%). PhySwin-B reaches 61.05% 61.05% (+0.80% over Dynamic Vis) and 18.1 13% SCS (+0. 13% over SkySense). Dynamic Vis achieves the highest precision (79.41 %)) but low recall (48.36%), while PhySwin-B offers a better balance (67.97% precision, 55.41% % recall). These gains, also shown in Fig. demonstrate that our physics-informed
'The top two results for each dataset are highlighted in bold. A denotes the gain over the best published RSEM baseline. indicates results reported from the corresponding literature.

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Table Change detection performance.
Model
OSCD
AF1%
Dyna.-S2
ASCS%
F1%
P%
R%
SCS%
SkySense
60.06
-0.19
18.00
HyperSIGMA
59.28
59.12
59.45
-0.97
SpectralGPT
54.29
52.39
57.20
-5.96
17.43
-0.57
ChangeMamba'
57.20
56.08
58.36
-3.05
Dynamic Vis
60.25
79.41
48.36
SpatSIGMAt
58.53
64.59
53.50
-1.72
SeCo
47.67
63.21
38.26
-12.58
16.00
2.00
CACo
$2.11
62.87
44.49
-8.14
16.53
-1.47
SarMAE
52.76
55.18
50.54
-7.49
16,20
-1.80
SatMAE++
55.31
58.07
52.80
-4.94
17.88
-0.12
PhySwin-T
58.74
65.93
52.96
-1.51
17.61
-0.39
PhySwin-B
61.05
67.97
55.41
+0.80
18.13
+0.13
Table 3: Multi-label and scene classification performance.
BigEarthNet
FMoW-S2
EuroSAT
Model
mAP%
AmAP%
Top-1 Acc%
AAcc%
0A%
40A%
SkySense
92.09
64.38
-0.86
SaIMAE
82,13
-9.96
63.84
-1.40
98.98
-0,23
SatMAE+
85.11
-6,98
65.24
0.00
99.04
-0.17
SpectralGPT
88.22
-3.87
64.21
-1.03
99.21
0.00
SeCo
87.81
-4.88
51.65
-13.59
95.63
-3.58
CACo
87.00
-5.09
50.72
-14.52
95.90
-3.31
PhySwin-T 86.63
-5.46
59.26
-5.98
97.25
-1.96
PhySwin-B
87.93
-4.66
63.11
-2.13
98.73
-0.48
pretraining reduces false positives and better captures subtle spectral changes, producing cleaner masks and more precise localization of small variations.
Multi-label and Scene Classification. Table shows PhySwin achieves competitive, near-SOTA results across all three benchmarks. On Big EarthNet,, PhySwin-B reaches 87.93% mAP (third, within 4. 4.16% of SkySense), while PhySwin-T records 86.63%. outperforming SeCo and CACo. On FMoW-S2, PhySwin-B achieves 63. % top-1 accuracy (2. 13% below SatMAE++), and PhySwin-T scores 59.26%. On EuroSAT, PhySwin-B attains 98. 78%73% overall accuracy (0.48% below SOTA SpectralGPT), and PhySwin-T posts 97. 97.25%. These small gaps likely reflect Swin's windowed attention emphasizing local context. We consider this an acceptable trade-off given PhySwin's 's faster inference and reduced memory compared to ViT-based RSFMs (discussed next).
4.3 Efficiency Analysis
Table compares
efficiency at 128 x 128 input on an RTX 2000 Ada GPU. CNNs like SeCo offer high throughput with low compute and memory but lower accuracy. ViTs improve accuracy but lose efficiency when scaled (e.g., SatMAE+ vs. SatMAE) or with more tokens (e.g., SpectralGPT vs. SatMAH), shown by higher FLOPs, memory and lower throughput. PhySwin balances these trade-offs: PhySwin-T delivers 14.4x higher throughput than SpectralGPT with 43.6x fewer FLOPs and 62.5% less memory. PhySwin-B exceeds SatMAE+ with 13.5x speedup and 64% memory reduction. These results show PhySwin improves efficiency over ViTs, approaching CNN-level efficiency without sacrificing accuracy advantages (as shown in Fig. TT).
4.4 Ablation Study
perform ablations on PhySwin-B to isolate contributions of each design choice. All exper- Losses
iments use the same evaluation heads and data splits as in Sec. Physically-Inforre Ablation. We ablate physics-ine ahysics-informed losses on PhySwin-B (Table The baseline without physics priors shows the lowest scores. Adding spectral smoothness (Lymoeth) reflectance bounding (Loound)

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Table Inference efficieney (feature extraction) 128 128 input, batch size 4, after 10 warm-up iterations on a single RTX 2000 Ada GPU. Type model; Params parameters; Throughput images/s; Mem = peak GPU memory. CACo is excluded as shares SeCo's ResNet50 backbone.
Model
Type
Params (M)
FLOPs (G)
Throughput (imgs/s)
Mem (GB)
SpectralGPT
VIT-B
85.4
87.2
76.4
SaIMAE
ViT-B
85.7
65.5
136.4
0.5
SatMAE++
VIT-L
303.2
232,6
42.8
1,4
ScCo
ResNet-50
23.5
1.4
2642.8
0.1
PhySwin-T
SwinV2-T
28.5
2.0
1102.0
0,3
PhySwin-B
SwinV2-B
87.9
7.6
576.8
0.5
Table 5: Ablation studies on physics-informed losses and masking schemes. Seg: SegMunich (mloU %). CD: OSCD (FI %). CLS: FMoW-S2 (Top-1 1 %). Throughput in images per second (img/s).
Variant
Seg (%)
CD (%)
CLS (%)
Mask type
Seg (%)
CD (%)
Throughput
No physics
47.73
53.37
57.66
Lsmooth only
50.91
58.99
61.93
SimMIM MixMAE
47.79
52.46
188.50
Lbound only
49.78
56.42
59.46
52.11
59.33
153.26
Lsmooth Lbound
52.32
61.05
63.11
Ours
52.32
61.05
149.84
Ablation of physics-informed losses.
(b) Ablation of masking schemes.
significantly improves performance, with Lsmooth obtaining larger gains by better capturing inter-band relationships. Combining both losses achieves the best results, confirming complementary effects. These results demonstrate that thysics-informan constraints enhance feature quality.
Pretraining Methods. Table|5b/compart pretraining methods for S2in beyond throughput. SimMIM shows highest throughput but lowest accuracy, [MASK] tokens offer less effective feature learning. MixMAE improves accuracy via dual reconstruction and better token use. Our method enhances MixMAE with SW-MSA and SW-Masking, achieving top accuracy with minimal throughput loss. These results highlight the advantage of effective information learning (MixMAE, ours), with SW-MSA providing additional global context.
Spectral Embedding Ablation. Table 6 evaluates five spectral embedding strategies. Since the spectral structure is ignored, the naive flattened processing has the highest throughput but the lowest accuracy. SpectralGPT's 3D Patch improves accuracy by splitting spectra into 3-band tokens, but significantly reduces throughput. SatMAE's Grouped Embedding (GE) uses one-third fewer tokens than SpectralGPT and boosts performance. Our token-efficient GE packs spectral groups into single embeddings, matching SatMAE GE accuracy with x higher throughput. Adding MaskSpec subspace masking further enhances robustness, delivering the best iccuracy-efficiene balance.
Hyperparameter Sensitivity. We tested the physics-loss weights À E 0..1, 0.5). 8 E {0.05, 0.1, 0.2], and MixMAE mixing ratios of 50%, 67% and 75%. Across these settings, segmenta- tion mloU and change-detection Fl vary by less than 2%, demonstrating that our defaults (A 0.25, 0.1, mix=50%) lie in a stable region.
Conclusion and Limitations
We presented PhySwin, novel foundation model for multispectral EO that integrates three key innovations: (1) physics-informee pretraining via spectral-smoothnes. and energy-conservale losses, (2) refined MixMAE tailored to Swin and (3) token- eeffcieff embedding with MaskSpec. Pretrained on over one million Sentinel-2 tiles and evaluated across various downstream benchmarks, ShySwwn achieves SOTA performance while significantly reducing both computational complexity and inference latency. We observe that model size correlates positively with FM performance. However, in ViT-based models, scaling these factors drastically increases fine-tuing and inference complexity, limiting practical deployment. Our results confirm that the hierarchical structure mitigates

---

Table Ablation embedding strategies (PhySwin-B Inference). 3D Patch follows SpectralGPT; Grouped Embedding (GE) group bands for embedding; MaskSpec adds random subspace masking,
Variant
Seg mloU (%) OSCD FI (%) FMoW-S2 Top-1 (%) Throughput (img/s)
Naive
43.21
44.76
52.73
599,38
3D Patch
53.47
61.34
64.13
144.32
GE (SatMAE)
54.22
60.11
63.77
155.98
GE (Ours)
52.03
60.89
63.02
544.25
GF+ MaskSpec (Ours)
52.32
61.05
63.11
584.58
these issues, offering multi-stage features beneficial for challenging EO tasks such as change detection. PhySwin further amplifies these strengths, delivering an optimal balance between computational efficiency and downstream task accuracy.
While PhySwin shows a modest performance gap on certain classification benchmarks, we attribute this mainly to SwinV2's hierarchical design, which is less suited for global pooling classification. Model scaling was not the focus of this study, though larger variants may yield further gains. Addi- tional limitations include the spectral spectral-smoothee smoothness prior's sensitivity to residual band misregistration and the use of single-timestamp pretraining, adopted to preserve an edge-friendly compute budget. Future work will extend PhySwin toward multi-temporal pretraining through efficient temporal patching and spatio-temporal masking, and explore misregistration-rrhuss smoothness variants, hy- brid architectures, and advanced radiative-transte constraints (e.g., BRDF models, atmospheric correction) for multi-modal fusion such as SAR-MS.
Acknowledgments and Disclosure of Funding
This work was supported by the Engineering and Physical Sciences Research Council (EP- SRC), United Kingdom, under the project Perfect Recollection for Clearer Insight (grant number EP/Y036077/1).
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Question:
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Answer: [Yes]
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While PhySwin is foundational research, it has positive potential for environ- mental monitoring, disaster response and agricultural management through more efficient EO analysis. No direct negative societal impacts misuse risks are identified.
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Justification: All datasets and models used are publicly available and properly cited (Sec- tion 4). Standard Earth observation datasets and baseline models (e.g., SatMAE, Spectral- GPT) are credited with references to the original papers and repositories; no proprietary or scraped data were used.
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Appendix A: Training and Implementation Details
Compute environment. Training was conducted two nodes equipped with 8 NVIDIA Quadro RTX 8000 GPUs (each with 48 GB memory). sexeedprecciion training (AMP) was enabled throughout. Distributed training was implemented via torchrun with NCCL backend, using socket-based transport for stability. Inference evaluations were performed on a single RTX 8000 GPU and also single NVIDIA RTX 2000 Ada GPU (8 GB VRAM).
Training configuration. We summarize the key hyperparameters in Table
Table 7: Training configurations for progressive pretraining.
Parameter
Stage 1: FMoW-S2
Stage 2: BigEarthNee-S2
Image resolution Epochs
x 96
128 128
200
100
Batch size per GPU
256
256
Total batch size
2048
2048
Learning rate
1 x 10-4
5 x 10-5
Weight decay
5 10-5
5 10-5
Warmup epochs
10
5
Scheduler
Cosine decay max_norm = 1.0
Cosine decay max_norm = 1.0
Gradient clipping
Steps per epoch
695
537 2685
Warmup steps
6950
Appendix B: Dataset Processing and Band Grouping
We apply a unified preprocessing pipeline across Sentinel-2 datasets used in this work. Key preprocessing steps include band selection, normalization, and spatial resizing, detailed in Table g Bands B01, B09, and B10 are excluded due their coarse 60 resolution. All retained bands are grouped into physically coherent spectral categories for structured encoding.
Table 8: The summary of common preprocessing and dataset-specific properties.
Common Preprocessing Across All Datasets
Normalization
Pixel values divided by 10,000
Valid reflectance range
Clipped to [o, 1.2]
Resizing method
Bicubic interpolation to fixed spatial size B01, B09, B00 (60 m resolution)
Excluded bands
Retained bands
B02-B08A, B11, B12 (10 bands)
Spectral groupings
Visible: B02, B03, B04
Red-Edge/NIR: B05-B08, B8A
SWIR: B11, B12
Dataset-Specific Details
Dataset
Tile Size
#Tiles
Task
FMoW-S2
96 96
712,874
Pretraining & scene-level classification
BigEarthNet-S2
128 128
549,488
Pretraining & multi-label classification Change detection
OSCD
96 96
336 pairs
SegMunich
128 128
8,430
Semantic segmentation
DynaS2
256 256
5,472 pairs
Multi-temporal change detection
EuroSAT
64 64
27,000
Land cover classification
Dataset licenses and sources.
BigEarthNet-S2:: htttps:/bbgeaas net Licensed under the Community Data Li- cense Agreement Permissive, Version 1.0.
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FMoW-S2: http:: htw.e//w....oo//oo//aaaattteeeea Released under the Functional Map the World Challenge Public License. Openly available for non-commercial research
use.
OSCD (Onera Satellite Change Detection): https:// wwt.e/aauuuiigggg..uu..e.e wtt.gu....ooscc..ll.. Publicly released for academic benchmarking; explicit livense provided.
DynaS2 (DynamicEarthNet Sentinel-2): ltttps://mediatum. ub. tum. dem..de2/6000 Licensed under Creative Commons Attribution-SharcAllikc 4.0 International (CC BY-
SA 4.0).
EuroSAT: httpps///gghbbb.oo///palllaaraaa Licensed under the MIT License.
SegMunich: tributed under Creative Commons Attrbuttion-KonCoomeriia 4.0 International (CC BY-NC 4.0).
Dis-
Appendix C: Expanded Ablation Studies
All ablations this section are conducted using the PhySwin-T model pretrained on the BigEarthNet- S2 dataset for 100 epochs. So, the reported results may slightly differ from those presented in Section Evaluation performed on three downstream tasks: SegMunich (semantic segmentation), OSCD (change detection), and EuroSAT (land cover classification).
C.1 Physics Loss Weights
Table 9: Sensitivity to physics-informed loss weights (A, B). Mixing ratio fixed 50%, and spectral grouping follows the default setting in Section 4. The best results are highlighted in bold.
(A,
SegMunich mloU (%)
OSCD (%)
EuroSAT OA (%)
10.1, 0.05)
47.97
56.33
96.64
(0.25, 0.1)*
48.46
56.98
96.88
(0.5, 0.2)
48.21
57.03
96.27
C.2 Mixing Ratio Effects
Table 10: Ablation on MixMAE spatial mixing ratio. Physics-informed loss weights are fixed at (A 0.25, 0.1), and spectral grouping follows the default setting in Section 4. The best results are highlighted in bold.
Mixing Ratio
SegMunich mloU (%)
OSCD (%)
EuroSAT OA (%)
50 %
48.46
56.98
96.88
67% %
45.29
55.63
96.01
75%
42.97
53.61
94.79
C.3 Spectral Grouping Variants
Table 11: Performance of different spectral grouping strategies. Physics loss weights are fixed at (A 0.25, 8 0.1), and the MixMAE spatial mixing ratio is fixed 50%. Default grouping is: Visible (B02-B04), RedEdge+NIR (B05-B08A), SWIR (B1 (B11-B12). The best two results are highlighted in bold.
Grouping Scheme
#Groups
SegMunich mloU (%)
OSCD (%)
EuroSAT OA (%)
Visible RedEdge++NIR SWIR
3
48.46
56.98
96.88
Visible+RedEdge NIR SWIR
3
48.37
57.41
96.04
Visible+NIR RedEdge SWIR
3
47.95
56.74
97.11
ViSible+SWIR RedEdge NIR
3
47.48
55.23
96.49
Visible+SWIR RedEdge+NIR
2
46.77
54.45
96.73
Visible RedEdge NIR SWIR
4
48.24
57.20
96.07
22

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Appendix D: Model Configurations
D.1 MixMAE Decoder Configuration
Linear| Project
X1
Transformer Block x8
LayerNorm
Encoder
Unmixing
Module
Linear
GELU
Linear
Linear
MixMAE
Positional Embeddings
Masks
Figure 4: PhySwin Decoder Structure.
PhySwin's decoder maps encoder's hidden states (dimension equal to the encoder's hidden size) into per-patch reconstructions via a lightweight Transformer stack, as shown in Fig. [ Concretely, the decoder first projects hidden features into a Dace = 312-dimensional space, then applies an unmixing module (two lincar layers with GELU nonlincarity) to disentangle mixed tokens. Two streams built by masking/unmaste summed with secubically-intepolattee 2D sine cosine positional embeddings. This fused sequence processed by Transformer blocks, followed by LayerNorm and a final linear prediction head that emits stride x C values per token (where stride 4 and 10 bands). All linear layers are Xavier-initialized and biases zeroed.
this output according to the MixMAE mask, then concatenated and
D.2 Downstream Plug-and-Play Heads
To ensure fair comparisons across backbones, adopt "plug-and-play" evaluation strategy: all models, including PhySwins and baselines, share the same task-specific head types, and only the encoder varies across models. This design isolates the effect of pretraining and encoder quality.
Table 12: Downstream task heads and objectives. UPerNet = Unified Perceptual Parsing Network; FPN Feature Pyramid Network; MI.P multi-layer perception. models use frozen encoder with plug-and-play heads.
Dataset
Task
Head Type
Objectives
SegMunich
Semantic segmentation Semantic segmentation
UPerNet decoder (FPN classifier)
Cross-entropy
Dyna.-S2
UPerNet decoder (FPN classifier)
Cross-entropy
OSCD
Hinary change detection
FPN 3-layer Cony
Binary cross-entropy
Dyna.-S2 (CD)
Semantic change detection
FPN + 3-layer Conv
Cross-entropy
GlohalAvgPool LayerNorm
FMoW-S2
Scene classification
Cross-entropy
+ 3-layer MLP
GlobalAvgPool LayerNorm
EuroSAT
Scene classification
+ 3-layer MLP
Cross-entropy
GlobalAvgPool LayerNorm
BigEarthNet
Multi-label classification
Binary cross-entropy
3-layer MI.P Sigmoid
Appendix E: Qualitative Examples
In this section, we provide additional qualitative examples for both semantic segmentation and change detection tasks. These remain highly challenging for RSFMs, and only few of the predictions achieve acceptable accuracy. Nevertheless, PhySwin consistently delivers superior overall quality than competing methods, as seen in Figures 5 and and in many cases finer detail.
That said, there still considerable room for improvement, especially on the most demanding benchmarks such as the Dyna.-S2 change detection challenge. Although PhySwin leads all SOTA
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Table 13: Downstream task training configurations.
Dataset
Input Size
Batch Size
Optimizer LR
Epochs
SegMunich
128x128
96
AdamW 5e-4
70
Dyna.-S2
256x256
36
AdamW W / le-4
120
OSCD
96x96
128
AdamW 1e-3
60
Dyna.-S2 (CD)
96x96
64
Adam W / 5e-4
70
FMoW-S2
96x96
128
Adam W 1e-5
100
EuroSAT
96x96
128
Adam W / 1e-3
100
BigEarthNet 128x128
96
AdamW 5e-5
100
baselines in the quantitative metrics reported in Table 2, its combined mask visualizations in Figure 8 remain visually inconsistent. Bridging this gap between numerical performance and perceptual fidelity will be a key focus for future RSFM development.
Pre-Change
Post-Change
Ground-Truth
CACo
SatMAE++
SpectralGPT
Phy-SwnnTT
PhySwin-B
Figure 5: Additional visualizations the OSCD dataset. Although pixel-level change detection remains challenging, PhySwin-B achieves the best performance among all baselines, producing fewer false positives.
Appendix F: Extended Results
To facilitate reproducibility and enable fair comparison with recent Earth Observation foundation models, we integrated the PhySwin encoder into the PANGAEA evaluation framework [Marsocci et al. 2024). We include representative subset of benchmarks for evaluation following the official training and evaluation protocols.
Segmentation and Change Detection. Table 14 summarizes results on four representative bench- marks using the PANGAEA settings. Each value represents the mean standard deviation across three random seeds (42, 177, 892). PhySwin-B consistently ranks among the top two performers across Sentinel-2-exclusive datasets, demonstrating its strong balance between accuracy and effi- ciency. PhySwin-B achieves top-tier performance across multiple PANGAEA segmentation and change detection benchmarks, while PhySwin-T remains competitive given its smaller capacity. These results further support PhySwin's officieency-rienteedd design.

---

Pre-Change
Post-Change
Ground-Truie
CACo
SatMAE++
SpectralGPT
PhySwin-T
PhySwin-B
Figure 6: Additional visualizations on Dyna.-S2 change detection. This benchmark even more challenging, and visually all RSFMs fail to produce coherent change masks. Although PhySwin-B attains the best quantitative results, these examples highlight the substantial room for improvement in RSFMs.
Table 14; PANGAEA segmentation and change detection performance (mean = std over three seeds).
Model
MADOS
CROPMAP
PASTIS
SENIFLOODS
CROMA
57.81 0.34
44.17 0.78
15.83 = 0.17
87.12 + 1.18
DOFA
64.40 0.19
38.48 0.53
12.58 + 0.19
85.42 0.83
PRITHVI
41.66 0.70
50.60
11.61 0.23
87.38
DINO
55.05 0.82
47.93
15.40
86.11
SatLasNet
52.80 + 0.68
4..04
13.29 0.21
82.96
Terramind-L
64.20 1.07
49.29
17.11
$7.45
PhySSwn-T
55.75 + 0.50
44.37
14.31
0.14
85.93 + 0.77
PhySwin-B
63.06 + 0.88
51.83 0.31
15.69 + 0.25
88.30 + 0.41
Classification and F1 Metrics. To provide more comprehensive evaluation, we additionally report macro-F1 alongside accuracy and mean average precision (mAP) under the unified PANGAEA recipe. For ETroSAT, macro-F1 is equivalent to accuracy since dataset single-label and balanced. For BigEarthNet, macro-F1 is reported on the standard subset used by GeoBench (20k training and 1k testing patches). Results are shown Table T5
Table 15: PANGAEA classification results (mean std).
Model
EuroSAT Acc
EuroSAT macro-F1
BigEarthNet mAP
BigEarthNNt macro-Fi
CROMA
93.13 + 0.18
93.13 + 0.18
72.50 + 0.73
64.90 + 0.65
DOFA
94.85 1 0.28
94.85 1 0.28
69.50 1: 0.39
62.40 + 0.25
PRITHVI
92.66 + 0.85
92.66 + 0.85
64.50 + 0.65
58.30 + 0.58
DINO
93.41 1.06
93.41 1.06
68.40 0.37
60.30 0.60
SatLasNet
97.81 0.95
97.81 + 0.95
70.00 0.70
64.80 0.30
Terramind-L
94.40 0.19
04.40
73.70 0.64
65.73 : 0.92
PhySwin-T
96.32 1: 0.16
96.32 4..32:00 0.16
0.16
68.20 + 0.68
61.90 + 0.24
PhySwin-B
98.17 0.19
98.17
71.60 + 0.43
64.10 + 0.27
The unified PANGAEA results confirm that PhySwin delivers near-stated star-state-of-the-ar accuracy and F1 scores while maintaining computational efficiency. Reported values are based on our own runs for PhySwin and publicly available checkpoints for other baselines.
Ablations on Physics-Informet Losses. To isolate the contribution of the physics-informed objectives, we conducted ablation experiments covering different pretraining variants: (i) Physics- only (no Spectral Group Masking or refined MixMAE), (ii) Group-only, (iii) Refmed-MixxMA-oolle (iv) Full configuration (all components), and two baselines: a SimMIMM-retrranng encoder and
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ROI
Ground-Trune
SeCo
SatMAE++
Spectra/GPT
PhySwin-T
PhySwin-B
Figure 7: Additional visualizations on SegMunich semantic segmentation. While most models capture the overall scene layout, PhySwin-B more accurately delineates object boundaries and small regions, demonstrating finer detail.
randomly initialized Swin V2 trained from scratch. Each variant was trained for 15 epochs using the PhySwin-T configuration and evaluated across three random seeds (42, 177, 892) to ensure statistical reliability.
Table 16: Ablation results isolating the contribution of physics-informed losses (mean + std).
Variant
OSCD F1 (%)
SegMunich mloU (%)
EuroSAT F1 (%)
Base (SimMIM)
49.31 + 0.99
42.64 : 0.85
92.83 = 0.37
Physics-only
52.23 0.26
44.71 + 0.89
94.05 + 0.94
Group-only
48.50 + 0.49
40.22 0.80
91.47 = 1.37
Miineed-MixMAB-nnly
49.06 0.98
43.10
92.61 = 1.85
Full (with all)
52.07 + 0.52
43.29 + 0.43 0.43
93.73 + 0.94
Base (scratch)
44.90 + 0.45
40.37 = 0.32
89.24 = 1.34
Physics-only pretraining improves consistently over the SimMIM baseline across all tasks (+2.9 Fl on OSCD, +2.1 mloU on SegMunich, and +1.2 F1 on EuroSAT), confirming that the physics-informed losses independently contribute to accuracy. The full configuration maintains these accuracy gains while providing the efficiency benefits of grouped embedding and refined MixMAF. Group-only and SefinedMMxxMARR the physics priors are the primary factor driving performance enhancements, while architectural modifications mainly improve efficiency.
variants do not account for the observed improvements, indicating that
Physics-Informed Informed Losses on MAE Backbones. To evaluate the generality of the proposed physics constraints beyond the Swin-hased design, we further applied the same physies losses to ViT-B backbone pretrained with standard MAE (without grouped embedding refined MixMAE). Both
26

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ROI
Ground- Truth
SeCo
SatMAE++
Spectra/GPT
PhySwin-T
PhySwin-B
Figure 8: Additional visualizations on Dyna.-S2 semantic segmentation. Despite the overall challenge. most models capture the coarse layout. PhySwin-B presents finer details.
models trained for 15 epochs under identical settings and evaluated on OSCD, SegMunich, and EuroSAT using the same random seeds.
Table 17: Impact of physics-informed losses on MAE pretraining (mean + std).
Backbone
OSCD FI (%)
SegMunich mloU (%)
FuroSAT FI (%)
VIT-B MAE VIT-B MAE Physics
50.46 + 0.51
42.00 0.84
94.78 1.90
52.41 + 0.52
44.17 + 0.88
95.20 + 0.95
Adding physics-informed regularization consistently improves the ViT-B MAE baseline across all tasks, demonstrating the general applicability of the proposed losses. These findings align with the results reported in the main paper, showing that the spectral-smoothness and energy- conservation constraints promote inter- band consistency and suppress out-of-range reconstructions, thereby encouraging physically plausible representations and faster adaptation during downstream fine-tuning.
27


Reviews:
[{"review_id": "poEB2z9b5c", "reviewer": "Reviewer_qjEV", "summary": "This paper introduces PhySwin, a foundation model developed for multispectral (MS) Earth observation (EO) imagery that addresses key limitations in current remote sensing foundation models (RSFMs), namely computational inefficiency and the neglect of physical/knowledge priors. The model incorporates three core innovations: physics-informed pretraining, which adds spectral smoothness and energy conservation constraints as regularizations; a refined MixMAE method adapted for the hierarchical SwinV2, utilizing shifted window attention to improve scalability and efficiency; and a token-efficient spectral embedding strategy that groups similar spectral bands and applies spectral group masking (MaskSpec) to retain spectral information without increasing token count. PhySwin is trained on +1M Sentinel-2 images, and it shows improved performance on key EO tasks such as semantic segmentation and change detection with computational complexity.", "questions": "- Spectral smoothness is a novel way of using physical information in the model training. However, misregistration of various spectral bands can punish $L_{smooth}$. How can this be addressed? Is this something that we need to accept as a limitation?\n\n- L141-142: It’s unclear how the claim “tokens attend only to others from the same source image, as dictated by M” is enforced. Equation (3) governs the reconstruction loss, but it does not prevent cross-source attention during encoding. Did you explicitly create an attention mask using M to restrict token interaction within each source? If so, I suggest adding 1–2 sentences in the text to clarify how this constraint is implemented in the encoder. In addition, the dual reconstruction loss in Eq(3) allows the model to learn to reconstruct missing information from real images, it would be good to add this. \n\n- Why Table 4 is missing SkySense, despite SkySense showing high performance in some tasks (Tables 2, and 3)?\n\n- [Minor] Figure 3 is missing a color legend for labels, please add.", "limitations": "Yes.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "The proposed model, PhySwin, is a novel advancement that integrates physically grounded learning in the training stage, incorporating radiometric constraints (bounded reflectance and spectral smoothness) to enhance generalization and interpretability. A masking strategy that combines spatial and spectral masking further strengthens feature resilience and richness. Its efficiency-focused architecture, built on SwinV2 and a refined MixMAE framework, delivers significant computational advantages. The model demonstrates enhanced performance across multiple Earth observation tasks, with gains such as +1.32% in segmentation mIoU and +0.80% in change detection F1 score. These benefits are validated through extensive experiments on diverse benchmarks. \nPhySwin some limitations too as it underperforms on all multi-label and scene classification tasks when compared to ViT-based baselines. Additionally, the study does not explore scaling up the model, leaving open the question of how larger PhySwin variants might perform on more complex or diverse tasks (while authors list this as a limitation). Finally, the paper does not report uncertainty estimates or statistical significance tests, making it difficult to evaluate the variability or robustness of the reported results.", "was_revised": true, "final_justification": "The authors have successfully addressed my questions, and provided new results that addresses the weaknesses I raised.", "rebuttal": "## Point 1: Classification performance clarification\n\nThank you for raising this. The cited relatively large **+4 pts** gap comes from **literature‑reported** Skysense numbers. **Skysense is not open‑sourced**, so we cannot evaluate it under the same protocol. Following other reviewers’ suggestions, we now use the **unified, open‑sourced PANGAEA/GeoBench** framework and report **macro‑F1** (in addition to Acc/mAP), with **mean ± std over 3 seeds (42/177/892)**.\n\n**TABLE 1: Classification under P/GEO (EuroSAT & BigEarthNet‑subset).**\n\n| Model | **EuroSAT** Acc | **EuroSAT** macro‑F1 | **BigEarthNet (subset)** mAP | **BigEarthNet (subset)** macro‑F1 |\n|---|---:|---:|---:|---:|\n| CROMA | 93.13 ± 0.18 | 93.13 ± 0.18 | ***72.50 ± 0.73*** | ***64.90 ± 0.65*** |\n| DOFA | 94.85 ± 0.28 | 94.85 ± 0.28 | 69.50 ± 0.39 | 62.40 ± 0.25 |\n| PRITHVI | 92.66 ± 0.85 | 92.66 ± 0.85 | 64.50 ± 0.65 | 58.30 ± 0.58 |\n| DINO | 93.41 ± 1.06 | 93.41 ± 1.06 | 68.40 ± 0.37 | 60.30 ± 0.60 |\n| SatLasNet | **97.81 ± 0.95** | **97.81 ± 0.95** | 70.00 ± 0.70 | **64.80 ± 0.30** |\n| PhySwin‑T | 96.32 ± 0.16 | 96.32 ± 0.16 | 68.20 ± 0.68 | 61.90 ± 0.24 |\n| **PhySwin‑B** | ***98.17 ± 0.19*** | ***98.17 ± 0.19*** | **71.60 ± 0.43** | 64.10 ± 0.27 |\n\n*Notes:* (i) GeoBench uses a **subset** of BigEarthNet (20k train / 1k test), so numbers differ from full‑dataset training; (ii) **Provenance** will be labeled in all tables (**ours (reproduced)** vs **from literature**).\n\nAs shown, **PhySwin‑B** remains **top‑tier** and aligns with our paper’s comparisons (excluding Skysense), while maintaining the **efficiency** needed for deployment‑level use. Similar discussions can be also seen in point 3 for reviewer **HaH9**.\n\n## Point 2: Uncertainty estimates & significance\n\nThank you for noting this. In our updated experiments we now **report mean ± std over 3 random seeds (42, 177, 892)** for all tasks and tables (including newly added experiments based on PANGAEA/GeoBench). Across seeds, results are **stable**, normally less than 1% (small std), and we will **add seed info and mean ± std** to table captions in the paper and supplement. Additional results can also be found in the tables included in our answers for other reviewers.\n\nThese additions address the reviewer’s request for uncertainty reporting and statistical support.\n\n## Point 3: Spectral smoothness vs. band misregistration\n\nThank you for the thoughtful question. We **agree misregistration can bias a spectral‑smoothness prior** if adjacent bands are not perfectly aligned.\n\nCurrently, in our pipeline, the smoothness loss is implemented as a **soft penalty** (with moderate weight) applied to the reconstructed reflectance vector per pixel. This means it serves as a gentle prior rather than a strict constraint. However, we acknowledge that residual misregistration remains a limitation of the current formulation. **As future work**, we plan to explore **misregistration‑robust smoothness variants**, or alternatively, more straightforward approaches such as data preprocessing or filtering methods to avoid this happens.\n\n## Point 4: Cross‑source attention constraint (L141–142) & dual reconstruction\n\nThank you for the question. We will clarify how we ensure \"tokens attend only to others from the same source image, as dictated by **M**.\"\n\nDuring pretraining with mixing, we build an **attention mask** from the mixing map **M** and apply it to **every self‑attention layer** (both W‑MSA and SW‑MSA). Concretely, if tokens *i* and *j* come from different source images according to **M**, the attention score entry is set to **the very large nagative value** before softmax (i.e., a **block‑diagonal attention mask** over sources). In addition, window partitioning packs tokens **per source** so that windows never span tokens from two sources; the mask remains active after the shift in SW‑MSA to prevent any cross‑source leakage.\n\nFor **Dual reconstruction (Eq. 3)**, we will add one sentence noting that the **dual reconstruction loss** reconstructs the masked regions of **each source image from its own unmasked context**, i.e., the model is trained to recover missing content from *real within‑source evidence*, not from cross‑source tokens.\n\nWe will include more details in Section 3 and new pseudo‑code in the Supplementary.\n\n## Point 5: Why Skysense is absent from Table 4\nAs noted in **Point 1**, **Skysense is not open‑sourced**, so we cannot reproduce it under our unified pipeline. **Table 4** includes only models we can run end‑to‑end.\n\n## Point 6: Figure 3 color legend\nThanks for catching this. We will **add a color legend** to Figure 3 and clarify the caption. Quick details:\n- **Top:** OSCD change‑detection masks (foreground/background).  \n- **Bottom:** **SegMunich** (13 LULC classes) and **Dyna.-S2** semantic segmentation—colors will follow each dataset’s **official palette** and be listed in the legend.\n\nWe will update the caption accordingly."}, {"review_id": "f6LQvI2hM5", "reviewer": "Reviewer_HaH9", "summary": "The paper proposes a new foundation model for multispectral remote sensing data which introduces three modifications with respect to previous work in its architecture/pre-training process: (1) it integrates radiometric physical constraints into the pre-training loss (bounding reflectance values to be on fixed range due to energy conservation), (2) it uses an efficient modified MixMAE scheme, which coordinates the masking patterns with the shifted windows in SWIN, (3) a spectral embedding technique that groups spectral bands and uses a lightweight embedding function per group that are then concatenated to create a single embedding vector for each spatial location, maintaining the final token count fixed, independent of the number of channels.\nThey compare this method with other foundation models for multispectral remote sensing data on four different types of downstream tasks: semantic segmentation (2 benchmark datasets), change detection (2 benchmark datasets), scene classification (2 benchmark datasets) and multi-label land cover classification (1 benchmark dataset), both in terms of accuracy metrics (appropriate for each task) as well as inference efficiency. They perform ablation studies to verify the effect of each of the modifications proposed also both in terms of accuracy metrics and on inference efficiency.", "questions": "1. Why have you ignored the temporal aspect of remote sensing images in pre-training? Do you think it is not important to model this? Would it be possible to modify your model to include multiple images as input?\n2. How have you selected the models to compare against? There are many other models (see link above) which have not been included such as DOFA, Prithvi-v2, DINO, AnySat.\n3. Thinking about a “physically-grounded” model for remote sensing, is bounding reflectance values to be on fixed range due to energy conservation the only physical constraint possible? \n4. Are there ways to trade-off accuracy versus efficiency in your model? What is the effect of the sizes of the network, number of parameters?", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The paper is well-written and describes very clearly what are the proposed improvements relatively to previous work and the rationale behind the changes.\n2. The proposed modifications seem technically sound.\n3. The experimental analysis and ablation studies are well designed. \n4. The accuracy results for semantic segmentation and change detection show that the model is on par or better than the other FMs compared.\n5. The efficiency results in terms of throughput in inference are very compelling.\n\nWeaknesses:\n1. The proposed changes are incremental and heavily build upon previous work. \n2. The claim of being “physically-grounded” is a little strong given that they are only introducing a bound on the reflectance value in the training loss). \n3. For the scene classification and multi-label land cover classification task, they say that the results are competitive “near-SOTA” in terms of accuracy, but in fact the differences are quite large in terms of percentage points (for BigEarthNet it is 4 percentage points), so it might not be worth having a faster model which is actually significant worse.\n4. There are many recent foundation models for remote sensing data (see https://github.com/Jack-bo1220/Awesome-Remote-Sensing-Foundation-Models) and they only compare against a few, leaving out ones which seem to have better results in terms of accuracy (DOFA, Prithvi-v2, DINO, AnySat,…), although it is difficult to compare because there are no standard benchmarks for this type of model (GeoBENCH is an attempt to establish that). \n5. Although there is a temporal aspect to remote sensing images, this paper completely ignores this, where most of the recent models take that aspect into account.", "was_revised": true, "final_justification": "The author's response has satisfactorily addressed the negative points I had raised. With the new results that they presented both in terms of the role of the \"physics-informedness\" of the model and comparisons with other SOTA models, I believe this is a technically solid paper with significant result for the area of foundation models for earth observation.", "rebuttal": "## Point 1: \"Physically‑grounded\" claim clarification\n\nThank you for raising this. We would like to clarify that our physical component comprises **two priors**, not only a bound:\n\n- **Spectral smoothness:** enforces continuity across **adjacent spectral bands**, reflecting the well‑documented smooth variation of surface reflectance with wavelength (Fig. 1b, §3.1).\n- **Energy bounds:** applies a **soft reflectance‑range prior** so reconstructions remain physically plausible (Fig. 1a, §3.1).\n\nAs suggested by reviewer **FjVd**, we also ran the new ablations as the evidence that **the priors matter (beyond architecture)**. More information can be found in our answers of reviewer **FjVd**.  \n\n**A) Isolating the physics priors within our setup** (PhySwin‑T, 15‑epoch for all variants due to compute limits). *(As suggested by reviewer **qjEV**, we report mean ± std over 3 seeds 42/177/892)*\n\n| Variant               | OSCD F1 %           | SegMunich mIoU %     | EuroSAT F1 %        |\n|----------------------|---------------------:|----------------------:|---------------------:|\n| Base (SimMIM)        | 49.31 ± 0.986        | 42.64 ± 0.853         | 92.83 ± 0.371        |\n| **Physics only**     | ***52.23 ± 0.261***    | ***44.71 ± 0.894***     | ***94.05 ± 0.940***    |\n| Group only           | 48.50 ± 0.485        | 40.22 ± 0.804         | 91.47 ± 1.371        |\n| Refined‑MixMAE only  | 49.06 ± 0.981        | 43.10 ± 0.862         | 92.61 ± 1.852        |\n| With ALL             | **52.07 ± 0.521**        | **43.29 ± 0.433**         | **93.73 ± 0.937**        |\n| Base (Scratch)       | 44.90 ± 0.449        | 40.37 ± 0.323         | 89.24 ± 1.339        |\n\nThis isolates the contribution of the priors: they improve accuracy **without** grouped embedding or refined MixMAE.\n\n**B) Adding the priors to a separate backbone** (ViT‑B + MAE, 15 epoch).\n\n| Backbone               | OSCD F1 %          | SegMunich mIoU %     | EuroSAT F1 %        |\n|-----------------------|--------------------:|----------------------:|---------------------:|\n| ViT MAE               | 50.46 ± 0.505       | 42.00 ± 0.840         | 94.78 ± 1.896        |\n| **ViT MAE + Physics** | **52.41 ± 0.524**   | **44.17 ± 0.883**     | **95.20 ± 0.952**    |\n  \nThese arise that the physics regularizers encourage **inter‑band consistency** and **suppress out‑of‑range reconstructions**, guiding features toward a **physically plausible direction** and faster adaptation to valid value ranges.\n\nFurthermore, to avoid overstatement, we will adjust the wording to **“physics‑informed priors”** and add explicit pointers to **Fig. 1a–b** and **§3.1** where these losses are defined and motivated.\n\n## Point 2: \"Incremental changes\"\n\nWe appreciate the concern and will make the **design rationale** more explicit. Our contributions target the dual goal of **accuracy + deployment‑level efficiency** for multispectral EO, and they work **together** rather than as small tweaks in isolation:\n\n- **Token‑efficient grouped spectral embedding.**  \n  Unlike standard patch embeddings whose token count grows with channels, our grouped spectral embedding **keeps the token count fixed** while preserving band structure via lightweight per‑group mappings and concatenation. This directly reduces memory/FLOPs without discarding spectral information. Meanwhile the randomly masking spectral groups during the pretraining ecourage the model to learn from different groups, avoiding the bias in features.\n\n- **Refined MixMAE that preserves SW‑MSA.**  \n  We coordinate MixMAE’s masking with Swin’s shifted windows, **retaining cross‑window context** and **eliminating [MASK] tokens** that otherwise slow hierarchical transformers. This lets us **pretrain Swin‑v2 faithfully** and then fine‑tune without modifying the architecture.\n\n- **Physics‑informed priors (two losses).**  \n  Beyond architecture, we introduce **spectral smoothness** (adjacent‑band continuity) and **energy bounds** (plausible reflectance range). These priors **improve accuracy independently** of architectural changes (see Point 1 ablations and the **ViT‑MAE + Physics** result).\n\n**Action we will take:** we will refine **design‑rationale paragraph** for mapping each component to its role (grouped embedding → token/compute efficiency; refined MixMAE → Swin‑compatible pretraining with high throughput; physics priors → accuracy/robustness), and we will put these connections earlier to avoid the impression of small, incremental tweaks. Thank you for raising this point.\n\n## Point 3: Classification results and \"near‑SOTA\" concern\n\nThank you for flagging this. The cited **+4 pts** comes from **literature‑reported** Skysense numbers; **Skysense is not open‑sourced**, so we cannot evaluate it under the **same** protocol/settings.\n\nTo ensure **scope and fairness**, and **as suggested by reviewer HaH9 and HFfE**, we moved classification to the open‑sourced **PANGAEA/GeoBench** framework and now report **macro‑F1** in addition to accuracy/mAP. Under this unified setup, we get **TABLE 1** in our response for reviewer **qjEV**.\n\nThese standardized, reproducible runs show that **PhySwin‑B is still top‑tier** compared with the most recent baselines while maintaining strong efficiency. We believe this can address the \"near‑SOTA\" concern under a unified evaluation protocol. \n\n**Our actions:** We will label **ours (reproduced)** vs **from literature** in all tables for transparency. Some strong baselines (e.g., non‑open‑sourced checkpoints) may reflect recipe differences.\n\n## Point 4: Baseline coverage concern\nThanks for pointing this out. To address it fairly and reproducibly, we moved our evaluation to the **PANGAEA/GeoBench** framework and added recent EO FMs to the comparison, following the unified recipe.\n\n**TABLE 1: PANGAEA results (S2 benchmarks).**\n\n| Model | MADOS | CROPMAP | PASTIS | SEN1FLOODS11 |\n|---|---:|---:|---:|---:|\n| CROMA | 57.81 ± 0.34 | 44.17 ± 0.78 | ***15.83 ± 0.17*** | 87.12 ± 1.18 |\n| DOFA | ***64.40 ± 0.19*** | 38.48 ± 0.53 | 12.58 ± 0.19 | 85.42 ± 0.83 |\n| PRITHVI | 41.66 ± 0.70 | **50.60 ± 0.21** | 11.61 ± 0.23 | **87.38 ± 0.95** |\n| DINO | 55.05 ± 0.82 | 47.93 ± 0.52 | 15.40 ± 0.18 | 86.11 ± 0.51 |\n| SatLasNet | 52.80 ± 0.68 | 45.04 ± 0.36 | 13.29 ± 0.21 | 82.96 ± 1.57 |\n| **PhySwin‑T** | 55.75 ± 0.50 | 44.37 ± 0.79 | 14.31 ± 0.14 | 85.93 ± 0.77 |\n| **PhySwin‑B** | **63.06 ± 0.88** | ***51.83 ± 0.31*** | **15.69 ± 0.25** | ***88.30 ± 0.41*** |\n\n**Takeaway.** Under a standardized pipeline, **PhySwin‑B is Top‑1/Top‑2 across all four tasks**, while preserving efficiency.\n\n**Reproducibility.** We use PANGAEA defaults **with S2 bands** and hyperparameter settings; runs can be reproduced with the following command:\n\n```bash\ntorchrun --nnodes=1 --nproc_per_node=1 pangaea/run.py \\\n  --config-name=train \\\n  decoder=seg_upernet preprocessing=seg_resize criterion=cross_entropy \\\n  task=segmentation task.trainer.n_epochs=80 batch_size=16 \\\n  encoder=ENCODER dataset=DATASET\n  ```\n\nNote: Some models (e.g., terramind_large) require manual fixes to download and run experiments, so we skip them for now. We are continuously adding those that can be executed in PANGAEA and will include any newly completed entries in the later discussion and revised version if required. More experimental settings can be found in our answers for reviewer **HFfE**.\n\n## Point 5: Temporal aspect\n\nThank you for raising this. We totally agree that **Temporal modeling is important** for EO (e.g., crop phenology, flooding dynamics).\n\n**Why we did not include it in this release.**  \nOur first objective was to balance **accuracy and deployment‑level efficiency** under a fixed token/compute budget (on‑satellite/edge constraints). Multi‑temporal pretraining multiplies the token count and memory footprint; we therefore prioritized an **efficient multispectral encoder** and validated it thoroughly under a standardized pipeline. Note that while pretraining is single‑timestamp, our **change‑detection downstream head** already consumes **bi‑temporal inputs** in fine‑tuning/evaluation following the benchmark recipe. Meanwhile, this performance ranks among the top in all SOTA benchmark tests.\n\nFor question **Is it important / can the model handle multiple images?** The answer is Yes. PhySwin is **readily extensible to multi‑temporal inputs**, and this is **under active development**. Concretely, we are trying to implement (i) **temporal patching/merging** to keep the token budget roughly fixed, and (ii) **spatio‑temporal masking** aligned with SW‑MSA. These are directly compatible with our PhySwin design. \n\nWe will explicitly state in the paper that this version focuses on **efficient pretraining**, and we will list **temporal extensions** (temporal patching/merging + spatio‑temporal masking) as **future work**. This roadmap preserves the efficiency goals while adding the temporal dimension.\n\n## Point 6: Accuracy–efficiency trade‑offs\n\nYes, PhySwin is able to trade accuracy for speed/memory.\n\n**Practical aspects to tune the trade‑off.**\n- **Model size:** choose **T** for edge/real‑time or **B** for higher accuracy with moderate extra cost.\n- **Token budget:** adjust **spectral grouping granularity** (fewer groups → fewer params/tokens; more groups → richer per‑band specialization).\n- **Swin window/shift settings:** larger windows capture more context at extra compute.\n\nFor example, across our new PANGAEA/GeoBench results (Tables 1–2), **PhySwin‑B** consistently outperforms **PhySwin‑T** on segmentation, change detection, and classification, while **PhySwin‑T** delivers higher throughput and lower memory (Table 4 in the paper). This shows **scaling model size helps accuracy**, but we intentionally keep both variants **compact** to meet our deployment constraints.\n\nWe will add a brief paragraph in the paper summarizing these tunes' effects to help readers select a configuration aligned with their compute budget."}, {"review_id": "SW7bMQjcma", "reviewer": "Reviewer_FjVd", "summary": "This work proposes to leverage physical properties of spectral channels in multispectral remote sensing images as a pre-training prior for efficient Remote Sensing Foundation Models. The authors argue that architectural optimizations alone are not sufficient to obtain accurate and resource-efficient Foundation Models for Earth Observation (EO), thus prompting the use of physical properties as regularization: a research venue that the authors reckon has been overlooked in recent advancements in the field. The SwinV2 vision transformer model is chosen as backbone to all downstream tasks, since authors argue it is the most appropriate to handle multi-scale EO images.\nThe paper introduces three main contributions:\n- Physical grounding achivied with two _radiometric priors_ computed on the reconstructed images produced during MAE pre-training; these priors serve as a regularizing loss signal.\n- Introduction of a refined version of MixMAE for hierarchical transformers.\n- Image embedding approach that retains the spectral structure of multispectral images without increasing the number of tokens produced. Channels are grouped into spectral bands; each group is embedded through its dedicated mapping; the final token is formed by concatenating the group embeddings. Moreover the paper introduces _Spectral Group Masking_, where group embedding subspaces of tokens are randomly masked; authros argue it may encourage feature robustness.  \n\nThe proposed model is evaluated on semantic segmentation, change detection and land cover classification. Experiments and ablations show that incorporating physical priors during pre-training effectively produces sensible features for downstream tasks. As for inference performance, the proposed method scores significant advancements with respect to attention-based models in terms of FLOPS and throughput, while maintaining comparable task performance, effectively approaching the efficiency level of ConvNets.", "questions": "**Suggestions**\n- For better clarity, please mark which results where replicated indipendently by you and which were reported from papers.\n- Even though fully masking spectral group feature subspaces is sensibly presented as a way to prevent the model from relying on any single spectral group, I think that it would be interesting to try masking single components of the embedding, for instance using a uniformly sampled mask, across spectral groups; such experiments could provide additional insights on the robustness of learned features and the modelling of relationships inside and across spectral groups.\n- I think the caption of figure 1(a) and 1(b) should be expanded with a more thorough explanation on what these two plot represent and how visualized data was produced.", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strenghts**:\n  - Integrating physics grounding with multispectral images is interesting, as each channel / spectral band can encode meaningful information for downstream tasks. \n  - The improvements on inference throughput obtained by applying the proposed token-efficient embedding technique is significant and adequately underlined in the experiments.\n  - Relevant qualitative examples are provided in the appendix to demonstrate strengths and shortcomings of the proposed approaches.\n\n**Weaknesses**:\n  - Ablations do not clearly isolate the effects of physical grounding from orthogonal architectural enhancements: looking at the performance reported in Tables 5 and 6, it seems that the ablation experiments on physics-informed losses were performed on models featuring both Spectral Group Masking and enhanced MixMAE (inferred by looking at the last row). It would be interesting to report the results of Table 5(a) obtained with a model pre-trained without refined MixMAE and Spectral Group Masking. I believe this would provide a fairer comparison with the results obtained by the current models.\n  - No experiments were conducted to assess the effectiveness of physics-informed loss as an extension to current state-of-the-art models pre-trained with MAE: I believe that it would be worth evaluating the effectiveness of physical grounding on current SOTA models pre-trained with MAE. Such addition would provide:\n    - a clear understanding of its impact on a broader list of setups;\n    - a relevant baseline to evaluate the effectiveness of both refined MixMAE and Spectral Group Masking, given that they operate independently from grounding.\n - Overall, I believe this work is significant, but it falls short in effectively articulating the true reasons behind its importance. The physical inspirations presented are unconvincing, as they consist of existing losses borrowed from other works without a clear connection to physics (i.e. smoothing or clipping the output of a model). Meanwhile, the architectural choices – arguably the primary drivers of the model's efficiency – are largely overlooked. A presentation that places greater emphasis on the architectural design would enhance the clarity of the paper and better highlight its contributions.", "was_revised": true, "final_justification": "The authors answered all my concerns.", "rebuttal": "## Point 1: Ablations isolating the effect of physics‑informed losses\n\nThank you for the suggestion. To isolate the contribution of the physics priors, we ran **physics‑only** pretraining (no Spectral Group Masking, no refined MixMAE), alongside **group‑only**, **refined‑MixMAE‑only**, and the full **with‑all** configuration. **As suggested by reviewer qjEV, all results are reported as mean ± std over three random seeds (42, 177, 892).**\n\n| Variant               | OSCD F1 %           | SegMunich mIoU %     | EuroSAT F1 %        |\n|----------------------|---------------------:|----------------------:|---------------------:|\n| Base (SimMIM)        | 49.31 ± 0.986        | 42.64 ± 0.853         | 92.83 ± 0.371        |\n| **Physics only**     | ***52.23 ± 0.261***    | ***44.71 ± 0.894***     | ***94.05 ± 0.940***    |\n| Group only           | 48.50 ± 0.485        | 40.22 ± 0.804         | 91.47 ± 1.371        |\n| Refined‑MixMAE only  | 49.06 ± 0.981        | 43.10 ± 0.862         | 92.61 ± 1.852        |\n| With ALL             | **52.07 ± 0.521**        | **43.29 ± 0.433**         | **93.73 ± 0.937**        |\n| Base (Scratch)       | 44.90 ± 0.449        | 40.37 ± 0.323         | 89.24 ± 1.339        |\n\n**Implementation details.**\n- Due to time constraints, we ran **15 epochs** for **PhySwin‑T** on each variant to demonstrate relative performance. Full training results will be presented in the final paper.\n- **Base (SimMIM):** native patch embedding and SimMIM pretraining.  \n- **Physics‑only:** same embedding as the base, but pretraining loss includes physical constraints.  \n- **Group‑only:** uses our grouped spectral embedding with SimMIM on the spatial dimension.  \n- **Refined‑MixMAE‑only:** uses our refined MixMAE pretraining with native embedding and no physics constraints in the loss.  \n- **With‑all:** combines refined MixMAE, physics priors, and grouped embedding.  \n- We also included a **non‑trained SwinV2 (scratch)** for comparison.\n\n**Takeaways.**\n- Physics‑only improves over the SimMIM base consistently across all tasks: **+2.92 F1** (OSCD), **+2.07 mIoU** (SegMunich), **+1.22 F1** (EuroSAT), confirming that physics‑informed objectives contribute independently.  \n- **With‑all** maintains gains over the base while enabling the **efficiency benefits** of grouped embedding and refined MixMAE.  \n- **Group‑only** and **refined‑MixMAE‑only** do not explain the observed accuracy gains, underscoring that the **physics priors are a major driver** of performance, while other components mainly deliver efficiency.\n\nWe will add a short note in §4.4 to clearly attribute accuracy gains to the physics‑informed losses and efficiency to the architectural choices.\n\n## Point 2: Physics‑informed losses on MAE SOTA backbones\nThank you for this suggestion. To test generality beyond PhySwin, we added our two physics losses to a ViT-B model pretrained with standard MAE (no grouped embedding, no refined MixMAE).\n\n| Backbone                | OSCD F1 %          | SegMunich mIoU %    | EuroSAT F1 %        |\n|------------------------|--------------------:|---------------------:|---------------------:|\n| ViT-B MAE                | 50.46 ± 0.505       | 42.00 ± 0.840        | 94.78 ± 1.896        |\n| ViT-B MAE + Physics  | **52.41 ± 0.524**   | **44.17 ± 0.883**   | **95.20 ± 0.952**    |\n\nWe can observe that the gains arise again. This is align with our statements in the paper that the physics regularizers encourage inter‑band consistency and suppress out‑of‑range reconstructions, guiding features toward a physically plausible direction and faster adaptation to valid ranges. We will add a brief note in §4.4 as a baseline for refined MixMAE and Spectral Group Masking.\n\n*(Compute note: these runs follow the same reduced‑epoch style, 15 epochs, used in our ablations; we did not retune hyperparameters for the ViT + physics.)*\n\n## Point 3: Clarifying importance, physics link and architectural emphasis\n\nThank you for this constructive critique. We will rebalance the presentation to make the motivation and contributions clearer.\n\n**Why it matters**   \nWe observe a growing number of foundation models emerging in EO, with a strong focus on scaling parameters and pretraining datasets to push performance. While this trend has yielded impressive accuracy, there is also a rapidly growing interest in **deployment‑level ML for EO**. This interest is motivated by the fact that **large amounts of EO data can be discarded or delayed because of the high cost and latency of communicating with ground stations**. Our work is designed with this balance in mind. We aim not only for strong accuracy but also for efficiency at both training/finetuning and inference. Specifically, we introduce a **grouped spectral embedding** to reduce token counts without sacrificing spectral structure and refine **MixMAE** to maintain Swin’s window‑shifted context while being more efficient during the pretraining. Together with our physics‑guided priors, these components address the dual challenges of learning meaningful multispectral representations and achieving the throughput/memory profile required for practical deployment.\n\n\n**Physics priors are not generic \"smoothing\" or \"clipping\"**  \n- **Spectral smoothness** encodes the well‑established observation that surface reflectance varies smoothly with wavelength; we implement this as a first‑order difference on *adjacent bands in spectral order* and apply it to the reconstructed reflectance during pretraining.  \n- **Energy bounds** encode the physical boundedness of reflectance, serving as a soft barrier that keeps reconstructions in a realistic range while tolerating sensor noise.  \n\nThese losses operate on **reconstructed reflectance vectors**, tying them directly to spectral physics rather than generic pixel heuristics. Our ablations (Point 1) and MAE‑backbone test (Point 2) show consistent gains **even without architectural changes**, indicating the priors improve representation quality in a plug‑in manner.\n\n**Architectural choices**  \n- **Backbone:** SwinV2 for hierarchical, windowed attention → near‑linear scaling and strong dense‑task transfer.  \n- **Refined MixMAE:** we retain **SW‑MSA** and coordinate masking with window shifts, preserving cross‑window context while avoiding [MASK] tokens.  \n- **Token‑efficient grouped spectral embedding (+MaskSpec):** preserves rich band structure **without increasing token count**, delivering high throughput with robust features.  \n\nOur efficiency results in the paper highlight that these choices approach ConvNet‑level throughput while remaining competitive on accuracy.\n\nWe will (i) move a concise design‑rationale paragraph to the end of §3, explicitly mapping each component to accuracy vs efficiency goals; (ii) add a short physics‑motivation paragraph in §3.1 linking the losses to spectral smoothness and bounded reflectance. We hope these changes make the importance and novelty clearer.\n\n## Point 4: Additional suggestions and clarity improvements\n\nWe thank the reviewer for these practical suggestions and will address each point:\n\n1. **Marking replicated results:** We will clearly label in all tables which results are independently reproduced by us and which are reported directly from the original papers.  \n2. **Alternative masking strategies:** While orthogonal to our current design, we agree that exploring single‑component masking (e.g., uniformly sampled masks across spectral groups) could offer additional insights into feature robustness and inter‑group relationships. We will explicitly note this as an interesting direction for future work.  \n3. **Figure 1 captions:** We will expand the captions for Figure 1a–c to better explain:\n   - **Figure 1a:** The bounded reflectance distribution in Sentinel‑2 multispectral data and how this motivates the **energy‑bound loss**.  \n   - **Figure 1b:** The smooth transitions across adjacent MS bands, showing empirical spectral differences and motivating the **spectral‑smoothness loss**.  \n   - **Figure 1c:** The trade‑off between accuracy and inference throughput for EO foundation models, positioning PhySwin’s improvements.  \n\n   These expanded captions will explicitly link the visuals to the physics‑informed objectives (§3.1) and the overall efficiency‑accuracy motivation of PhySwin."}, {"review_id": "JbG9tKioKX", "reviewer": "Reviewer_HFfE", "summary": "PhySwin uses a Swin v2 architecture with a novel MAE pretraining process that retains Swin sliding window self-attention without using inefficient mask tokens. Additionally, it introduces two \"physically\" based regularizations to smooth MAE reconstruction across the channel dimensions and bind them to realistic values.", "questions": "- Please include the F1 score for the classification tasks on BigEarthNet and EuroSAT. Without these metrics, the evaluation remains incomplete and may justify a lower score.\n- Please report results on additional S2-exclusive benchmarks (e.g., GEO-Bench, PANGAEA) and compare against recent EO foundation models. Addressing this point could lead to an increased review score.\n- The claim on line 288 asserts that pretraining data volume, quality, and token count correlate with performance. Please provide experimental evidence to support this statement or remove it. Failure to do so may lead to a reduced evaluation score.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\nThe paper introduces three novel improvements that all show significant performance or throughput improvements. The proposed Refined MixMAE pretraining, which retains SW-MSA, is an elegant and natural extension of the original MixMAE pretraining. It fully retains the Swin v2 architecture while bringing noticeable improvements in fine-tuning performance at almost no training cost. The Token-Efficient Grouped Spectral Embedding is a very clever way of giving the patch encoder the ability to process very different bands in different ways at almost no cost.\n\nWeaknesses:\nThe results section is lacking in several aspects:\n- While the inclusion of change detection benchmarks is positive, the evaluation on semantic segmentation and classification is insufficient in both scope and comparative analysis.\n    - Multiple S2-exclusive benchmarks are missing, see GeoBench[1] and PANGAEA[2] for some classical EO benchmark.\n    - There is a lack of comparison to recent EO foundation models such as SatLas (Swin-based), Dofa, Croma, Galileo, Panopticon, AnySat, and TerraMind, many of which are already evaluated on GeoBench[1] and PANGAEA[2].\n- Classification results are reported only using accuracy, which is saturated (over 90% for both BigEarthNet and EuroSAT); additional metrics like F1 score should be included.\n- The evaluation heads used are only described in the supmat, a reference to the supmat should at least be included in 4.1.\n\nConsidering the proposed approach is only trained on and evaluated with S1 and S2, it can hardly be called a foundation model for EO. The massive multimodality of EO data is one of the main challenges toward creating a proper foundation model for EO. Moreover, PhySwin is both pretrained and evaluated on FMoW-S2 and BigEarthNet-S2, which is a clear limitation for properly evaluating the pretrained model's ability to adapt to slight domain shifts.\n\nWhile the regularizations proposed are logical and bring improvements, their physical reality is questionable. $L_{smooth}$ is independent with respect to the $\\Delta\\lambda$ between two bands, and $L_{bound}$ is a simple regularization to the $[0,1.2]$ interval.\n\nIn some tables, multiple results are **bold**. Instead, second-best results should be underlined in all tables.\n\nThe authors claim on line 288: \"pretraining data volume and quality, and token count correlate positively with FM performance,\" yet no experiments validate this claim.\n\n[1] GEO-Bench: Toward Foundation Models for Earth Monitoring, Lacoste et al.\n\n[2] PANGAEA: A GLOBAL AND INCLUSIVE BENCHMARK FOR GEOSPATIAL FOUNDATION MODELS, Marsocci et al.", "was_revised": true, "final_justification": "This paper arguably makes four contributions:\n- An S2-only single-timestep encoder: This has limited utility, as most EO tasks benefit from time series and multimodal data.\n- An MAE pre-training approach for Swin that preserves Shifted Window Attention: While valuable, it is evaluated in a very narrow setting and would benefit from broader experiments on standard benchmarks.\n- A grouped patch encoding: This shows strong results but is evaluated only on S2 data, while other data types (e.g., hyperspectral) that could also benefit are regrettably omitted.\n- Two regularizations for EO MAE pre-training: Again, the evaluation is limited to S2 data and only two architectures (Swin and ViT). Ideally, these regularizations would be assessed on existing EO foundation models pre-trained with MAE. I acknowledge that this may be impractical due to technical constraints.\n\nNevertheless, I understand the authors’ decision to focus on a simple setup (S2 single-date) and to pre-train the strongest possible encoder within that scope. The two main limitations of the original manuscript (overly ambitious wording and an incomplete evaluation) have been satisfactorily addressed through the rebuttal/discussion process. While a broader scope might have yielded a more compelling paper, the revised manuscript does not exhibit major flaws warranting rejection. I update my rating to borderline accept.", "rebuttal": "We thank the reviewer for pointing us to PANGAEA/GeoBench (also suggested by reviewer **HaH9**). To facilitate reproducibility and fair comparison, we integrated our **PhySwin** encoder into the PANGAEA evaluation framework and re‑ran experiments under its unified training/evaluation recipes. Due to time constraints and a few checkpoints/datasets that required extensive manual fixes to download or run, we temporarily skipped them. We believe the results below already provide a comprehensive and representative picture of PhySwin’s performance. \n\nWe are **continuously running the remaining experiments**, including integrating our original baselines (**SeCo**, **CaCo**, and **SatMAE**) into PANGAEA to **unify the benchmarks and baselines** (an extra benefit beyond our core contribution). We will add the remaining items in the final paper.\n\n**TABLE 1: New PANGAEA results (mean ± std from three random seeds (42, 177, 892) as suggested by reviewer qjEV).**  \nWe will denote the best results in bold and the second-best results as underlined in the paper. Here, since OpenReview does not support underlining, we used ***bold+italic*** for the best results and **bold** for the second-best results.\n\n| Model | MADOS | CROPMAP | PASTIS | SEN1FLOODS11 |\n|---|---:|---:|---:|---:|\n| CROMA | 57.81 ± 0.34 | 44.17 ± 0.78 | ***15.83 ± 0.17*** | 87.12 ± 1.18 |\n| DOFA | ***64.40 ± 0.19*** | 38.48 ± 0.53 | 12.58 ± 0.19 | 85.42 ± 0.83 |\n| PRITHVI | 41.66 ± 0.70 | **50.60 ± 0.21** | 11.61 ± 0.23 | **87.38 ± 0.95** |\n| DINO | 55.05 ± 0.82 | 47.93 ± 0.52 | 15.40 ± 0.18 | 86.11 ± 0.51 |\n| SatLasNet | 52.80 ± 0.68 | 45.04 ± 0.36 | 13.29 ± 0.21 | 82.96 ± 1.57 |\n| **PhySwin‑T** | 55.75 ± 0.50 | 44.37 ± 0.79 | 14.31 ± 0.14 | 85.93 ± 0.77 |\n| **PhySwin‑B** | **63.06 ± 0.88** | ***51.83 ± 0.31*** | **15.69 ± 0.25** | ***88.30 ± 0.41*** |\n\n**Takeaways.** PhySwin‑B is consistently ranking among the top 2 performers across all benchmarks with S2-exclusive data. PhySwin‑T is competitive given its smaller capacity, supporting our efficiency‑first design.\n\nWe will release our PANGAEA configs and scripts for these runs to ensure full reproducibility. We also fixed our table styling as suggested (bold best, underlined second‑best) and will maintain this convention throughout the paper and supplement.\n\n## Point 1: F1 scores is missing\n\nThanks for the suggestion. We now **report macro‑F1 (mean ± std)** alongside accuracy/mAP under the unified PANGAEA recipe.\n\n- **EuroSAT:** single‑label and balanced, so **macro‑F1 is equivalent to accuracy**.  \n- **BigEarthNet:** multi‑label and long‑tailed.  \n\nUnder the PANGAEA settings, **PhySwin‑B ranks Top‑2 in Acc and mAP** (see **TABLE 2**), and shows only slightly lower performance compared to SOTA baselines. As we noted to reviewers **qjEV** and **HaH9**, **Skysense is not open‑sourced**, so the ~4% gap in our paper may stem from setting differences. These results, produced using the unified and open‑sourced PANGAEA framework, demonstrate that our method incurs only a minor compromise in classification performance but still top-tier, which is consistent with the findings reported in **Table 3** of our paper.\n\nWe will explicitly label which results are from our experiments and which are taken from the literature in the revised version (as suggested by reviewer **FjVd**). This will give a clearer picture and may further justify our method.\n\n**TABLE 2: Current classification results (mean ± std).**\n\n| Model | **EuroSAT** Acc | **EuroSAT** macro‑F1 | **BigEarthNet (subset)** mAP | **BigEarthNet (subset)** macro‑F1 |\n|---|---:|---:|---:|---:|\n| CROMA | 93.13 ± 0.18 | 93.13 ± 0.18 | ***72.50 ± 0.73*** | ***64.90 ± 0.65*** |\n| DOFA | 94.85 ± 0.28 | 94.85 ± 0.28 | 69.50 ± 0.39 | 62.40 ± 0.25 |\n| PRITHVI | 92.66 ± 0.85 | 92.66 ± 0.85 | 64.50 ± 0.65 | 58.30 ± 0.58 |\n| DINO | 93.41 ± 1.06 | 93.41 ± 1.06 | 68.40 ± 0.37 | 60.30 ± 0.60 |\n| SatLasNet | **97.81 ± 0.95** | **97.81 ± 0.95** | 70.00 ± 0.70 | **64.80 ± 0.30** |\n| PhySwin‑T | 96.32 ± 0.16 | 96.32 ± 0.16 | 68.20 ± 0.68 | 61.90 ± 0.24 |\n| **PhySwin‑B** | ***98.17 ± 0.19*** | ***98.17 ± 0.19*** | **71.60 ± 0.43** | 64.10 ± 0.27 |\n---\n***Note***, GeoBench uses only a subset of the BigEarthNet dataset (20,000 patches for training and 1,000 for testing), so results may differ from those obtained using the full dataset in our original training‑testing setup. \n\nWe will update the full classification table in the revised version.\n\n## Point 2: Additional benchmarks & comparisons\n\nPlease see **TABLE 1** for the new results on benchmarks against recent EO models.  \n**These results align with our paper’s conclusions**: PhySwin consistently ranks top‑1/top‑2 while remaining efficient.\n\nAll results are **reproducible** with PANGAEA’s default recipe **using S2 bands** and the command below (3 random seeds: 42, 177, 892):\n\n```bash\ntorchrun --nnodes=1 --nproc_per_node=1 pangaea/run.py \\\n  --config-name=train \\\n  decoder=seg_upernet preprocessing=seg_resize criterion=cross_entropy \\\n  task=segmentation task.trainer.n_epochs=80 batch_size=16 \\\n  encoder=ENCODER dataset=DATASET\n```\nReplace ENCODER/DATASET with the model and dataset names in `configs/encoder` and `configs/dataset` used in TABLE 1. The experiments are run on RTX 5090 GPU 24GB.\n\n## Point 3: Claim on pretraining data volume/quality/token count (line 288)\nWe appreciate the reviewer’s attention to this statement. Since none of our results or conclusions depend on this statement, we will **remove the claim from the paper (main text and supplement)** and **tighten the wording** to avoid any over‑statement. We will treat scaling effects as **future work** and only report such correlations once supported by dedicated experiments.\n\n## Point 4: Evaluation heads clarity\n\nWe agree and will **clarify the evaluation heads in the main text**. We will also add the exact configs used (encoder/head/loss) to ensure reproducibility in the Supplementary.\n\n## Point 5: Multimodality scope clarity\nThank you for raising this. To avoid over‑reach, we will **clarify the scope in the title/abstract and main text**, positioning PhySwin as an **efficient multispectral encoder pretraining method** rather than a full EO foundation model.\n\nSince our initial focus was on developing an **on‑satellite, deployment‑level efficient solution**, multimodality was not included in this first stage. With the progress we have made, we are now **actively working on enhancing performance with multimodal strategies** and exploring efficient solutions in this direction.  Furthermore, we believe our current settings are valuable for future multimodal training, as stronger S2 representations could benefit the overall multimodal system. We will continue exploring this angle in future work.\n\n## Point 6: Physical regularizations\nThank you for the thoughtful feedback. We agree our wording can better convey the intent and limits of the two regularizations. Our goal is to **inject physics‑motivated priors** into pretraining, not to claim full radiative‑transfer modeling.\n\n- **Spectral smoothness:** leverages the well‑established property that surface reflectance varies smoothly with wavelength (Fig. 1b, §3.1), promoting stable inter‑band relationships and more physically consistent features. Our implementation operates on **adjacent S2 bands in spectral order**; while it does not explicitly weight by wavelength spacing Δλ, we will state this modeling choice and note Δλ‑aware weighting as future work.\n\n- **Energy bounds:** enforce the physical principle that surface reflectance is bounded (Fig. 1a, §3.1), guiding the model toward realistic ranges and avoiding unphysical reconstructions. We will clarify that this is a **soft, physics‑motivated barrier** (upper bound relaxed to tolerate noise).\n\nTable 5a shows that each prior improves performance and their combination yields the strongest gains, confirming complementarity. We will make this physical motivation and the above clarifications explicit in the revised manuscript."}]

Author rebuttal:

Meta-review: This paper presents PhySwin, a foundation model for multispectral data that integrates physical priors and demonstrates computational efficiency.

The work is commended for its strategic masking strategy as well as effective integration of physical learning in the training stage.

Weaknesses included desire for further details on experimental results, which were provided in the rebuttal period. Some other masking strategies suggested remain to be tested.

Overall, all reviewers agreed this is a strong contribution and carefully discussed the rebuttal with authors with appropriate technical detail, upon which all major concerns were addressed.
Decision: Accept (poster)