🌍 COP-GEN

Latent Diffusion Transformer for
Copernicus Earth Observation Data

Generation Stochastic by Design

Miguel Espinosa1, Eva Gmelich Meijling2, Valerio Marsocci2, Elliot J. Crowley1, Mikolaj Czerkawski3
1University of Edinburgh, 2European Space Agency (ESA), 3Asterisk Labs
2025
Paper Code Model Collection arXiv
COP-GEN generates diverse outputs

Figure 1: Conditional generation of Sentinel-2 L2A imagery from DEM and LULC inputs (geolocation is shown only for reference, not used for conditioning). COP-GEN produces diverse and physically consistent outputs, demonstrating variability in spectral appearance, illumination, and atmospheric conditions while preserving topographic and land-cover constraints. This highlights the model's ability to capture one-to-many relationships inherent in multimodal Earth Observation.

Abstract

The domain of Earth observation is home to hundreds of petabytes of observational data from a plethora of diverse sensing instruments. Naturally, conditional mappings between subsets of available sensors are useful for interpreting the data. However, these conditional mappings are generally not injective and should be parameterised as data distributions.

COP-GEN is the first generative model for Earth observation that can approximate these multi-modal distributions owing to its latent diffusion transformer architecture. Consequently, unlike its state-of-the-art counterparts, COP-GEN can generate highly diverse distributions that represent the diversity of the underlying data more faithfully.

🎯 Key Insight: Cross-modal mappings in Earth observation are inherently one-to-many. A given terrain or land cover can correspond to many physically plausible optical, radar, or atmospheric realisations. COP-GEN captures this full distribution.

Stochastic vs Deterministic

Relationships between Earth observation modalities are inherently non-injective: identical conditioning information—such as terrain elevation or land-cover class—can correspond to multiple physically plausible observations. Such mappings are inherently one-to-many, as shown in Figure 1.

Existing multimodal Earth observation models are typically deterministic—given the same input conditions, they always produce the same output. Models optimized to minimize pointwise error against a single ground truth inevitably regress toward conditional means, suppressing variability that is physically relevant and present in real-world observations.

Stochastic generative modeling provides an alternative route. By learning joint probability distributions across sensors, generative models can estimate multiple physically plausible realisations of a scene conditioned on a subset of input modalities. This approach aligns with remote sensing practice, where environmental processes are dynamic, observations are most often incomplete, and many different outputs are valid.

COP-GEN Architecture

COP-GEN is a unified multimodal generative model that combines modality-specific latent encoders with a shared transformer-based diffusion backbone. It operates on six modalities at their native resolutions:

  • Sentinel-2 L2A & L1C — Multi-spectral optical imagery (10m, 20m, 60m bands)
  • Sentinel-1 RTC — Radar backscatter (10m)
  • Digital Elevation Model (DEM) — Terrain elevation (30m)
  • Land Use / Land Cover (LULC) — Semantic classes (10m)
  • Timestamp & Geolocation — Temporal and spatial context
COP-GEN Architecture
Figure 3: COP-GEN architecture, training, and inference overview. Multimodal inputs (optical, radar, elevation, land-cover, geolocation, and timestamps) are encoded into latent representations using modality-specific VAEs (or directly tokenized for scalar inputs). All tokens, augmented with modality-specific diffusion timestep embeddings, are processed by a shared transformer diffusion backbone. The model is trained to jointly predict noise for all modalities. At inference, modalities can be either sampled from noise or fixed at timestep zero, enabling both unconditional generation and flexible any-to-any conditional translation across modalities.
🔍 Click image to zoom

Why COP-GEN?

  • Stochastic by design: Captures the full distribution of physically plausible outputs, not just the conditional mean
  • Any-to-any generation: Generate any subset of modalities conditioned on any other subset — zero-shot modality translation without task-specific retraining
  • Native resolutions: Each modality is processed at its original spatial resolution, preserving physical structure without aggressive resampling
  • Unified architecture: Single transformer backbone handles all modalities through a shared token sequence with cross-modal attention
  • Calibrated uncertainty: Output variance naturally narrows as conditioning becomes more informative

Results

📊 Qualitative Results

Distribution Narrowing Under Increased Conditioning

As more conditioning modalities are provided, COP-GEN appropriately narrows its output distribution. Starting from DEM-only conditioning, we incrementally add LULC, S1 RTC, timestamps, and geolocation. The generated spectral distributions systematically converge toward the ground truth.

Distribution narrowing
Figure 4: Effect of progressively increasing input conditioning on generation quality. As additional modalities are provided, generated samples better align with the ground-truth distribution. Even under sparse conditioning, COP-GEN covers the true reflectance range without collapsing prematurely.

Spectral Fidelity Across Land-Cover Classes

COP-GEN learns physically meaningful spectral relationships. Per-pixel spectral responses closely match the characteristic signatures of different land-cover types across all Sentinel-2 bands.

Band Infilling

COP-GEN can also perform band infilling — generating missing spectral bands given a subset of available bands. This is useful for completing incomplete observations or synthesizing bands that were not captured by the sensor.

Band infilling
Figure 6: Band infilling experiment. Given visible bands and infrared, COP-GEN generates the remaining spectral bands, demonstrating its ability to learn cross-band dependencies and complete partial observations.

Conditional Generation

Additional examples of DEM + LULC → Sentinel-2 L2A generation, demonstrating COP-GEN's ability to produce diverse, high-quality outputs across different geographic regions and land-cover types.

📈 Quantitative Results

We adopt a Peak-Capability (oracle) evaluation protocol: for each test tile, we generate multiple independent samples and report the best-matching generation. This isolates the model's representational capacity from stochastic variance and answers: does the model's learned distribution contain high-fidelity realisations consistent with the ground truth?

Peak Capability Analysis

Target Metric Peak perf. (Best per Tile)
COP-GEN TerraMind
DEM MAE 26.80 145.62
SSIM 0.45 0.44
LULC Top-1 0.84 0.80
mIoU 0.42 0.55
S1RTC MAE 2.63 2.64
PSNR 16.83 19.65
S2L1C MAE 0.02 0.11
PSNR 21.16 12.77
S2L1C† MAE 0.05 0.12
PSNR 13.92 12.68
S2L2A MAE 0.02 0.10
PSNR 22.47 17.46
S2L2A‡ MAE 0.06 0.10
PSNR 14.40 16.18
LatLon Mean km 98.35 94.25

Table 1: Tile-Level Peak Capability Analysis. We report the oracle performance (best generation selected per tile) to demonstrate the upper bound of generation quality. Bold indicates the best result. † S2L2A not present among inputs. ‡ S2L1C not present among inputs.

Leave-One-Out Analysis

We analyze the impact of removing individual conditioning modalities to reveal the physical couplings learned by the model.

Target Removed Metric COP-GEN TerraMind
DEM w/o LatLon MAE 47.44 140.01
w/o LULC MAE 46.96 140.80
w/o S1RTC MAE 54.85 140.86
w/o S2L1C MAE 51.85 146.00
w/o S2L2A MAE 45.78 146.71
LULC w/o LatLon Top-1 Acc 0.81 0.80
w/o DEM Top-1 Acc 0.80 0.80
w/o S1RTC Top-1 Acc 0.79 0.80
w/o S2L1C Top-1 Acc 0.80 0.80
w/o S2L2A Top-1 Acc 0.80 0.80
S1RTC w/o LatLon MAE 2.70 2.63
w/o DEM MAE 2.75 2.63
w/o LULC MAE 2.76 2.63
w/o S2L1C MAE 2.70 2.64
w/o S2L2A MAE 2.68 2.62
S2L1C w/o LatLon MAE 0.02 0.11
w/o DEM MAE 0.02 0.11
w/o LULC MAE 0.02 0.11
w/o S1RTC MAE 0.02 0.11
w/o S2L2A MAE 0.06 0.12
S2L2A w/o LatLon MAE 0.02 0.10
w/o DEM MAE 0.02 0.10
w/o LULC MAE 0.02 0.10
w/o S1RTC MAE 0.02 0.10
w/o S2L1C MAE 0.07 0.10
LatLon w/o DEM Mean km 210.54 90.67
w/o LULC Mean km 188.70 95.50
w/o S1RTC Mean km 173.09 78.23
w/o S2L1C Mean km 193.43 138.83
w/o S2L2A Mean km 182.45 77.41

Table 2: Tile-Level Leave-One-Out Analysis. COP-GEN shows clear dominance in DEM reconstruction (MAE) and optical bands (S2L1C/S2L2A), while TerraMind demonstrates stronger localization capabilities (LatLon).

Conclusion

We present COP-GEN, the first multimodal generative model for Earth observation that successfully combines multiple Copernicus modalities into a single stochastic framework. By learning joint probability distributions across sensors, COP-GEN can estimate multiple physically plausible realisations of a scene conditioned on any subset of input modalities.

This work is as much about the architecture as it is about how generative Earth observation models should be evaluated — moving beyond single-reference metrics to embrace the inherent uncertainty of cross-modal mappings.

BibTeX

@article{espinosa2026copgen,
  title={COP-GEN: Latent Diffusion Transformer for Copernicus Earth Observation Data},
  author={Espinosa, Miguel and Gmelich Meijling, Eva and Marsocci, Valerio and Crowley, Elliot J. and Czerkawski, Mikolaj},
  journal={arXiv preprint},
  year={2026}
}