RAMEN: Resolution-Adjustable Multimodal Encoder for Earth Observation

Paper | Code

RAMEN is a resolution-adjustable multimodal encoder that learns a shared visual representation across Earth Observation (EO) data in a fully sensor-agnostic manner. It treats modality and spatial/temporal resolutions as key input features, enabling coherent analysis across modalities. Its main methodological contribution is to define spatial resolution as a controllable output parameter, giving users direct control over the desired level of detail at inference and allowing explicit trade-offs between spatial precision and computational cost.

Key features

• 🛰️ Sensor-agnostic foundation model: RAMEN supports any kind of multispectral, SAR or elevation maps modalities. Just specify input shape, channels and original spatial resolution (GSD) !
• 🔧 Adjustable feature map resolution: Customize the resolution of feature maps to suit specific downstream tasks and computational constraints.
• 🌍 Multimodal data fusion: Effectively combine data from multiple modalities into a unified representation.

PANGAEA Bench evaluation

All downstream tasks results presented in RAMEN were conducted using the PANGAEA Benchmark. We report here the main results obtained on eight tasks.

Model	BurnSr	MADOS	PASTIS	Sen1Fl11	DEN	CTM-SS	SN7	AI4Farms	Avg. mIoU	Avg. Rank
CROMA	82.42	67.55	32.32	90.89	38.29	49.38	59.28	25.65	55.72	6.50
DOFA	80.63	59.58	30.02	89.37	39.29	51.33	61.84	27.07	54.89	7.50
TerraMind-B	82.42	69.52	40.51	90.62	37.87	55.80	60.61	28.12	58.18	4.25
TerraMind-L	82.93	75.57	43.13	90.78	37.89	55.04	59.98	27.47	59.10	3.75
RAMEN (ours)	85.02	69.72	42.29	91.03	39.85	53.27	60.31	38.78	60.03	2.63

More informations on how to reproduce results and implement RAMEN in PANGAEA can be found in the pangaea-bench folder.

Citation

If you use RAMEN, please cite our paper:

@article{RAMEN,
  title={{RAMEN}: Resolution-Adjustable Multimodal Encoder for Earth Observation},
  author={Nicolas Houdré and Diego Marcos and Hugo Riffaud de Turckheim and Dino Ienco and Laurent Wendling and Camille Kurtz and Sylvain Lobry},
  journal={arXiv preprint arXiv:2512.05025},
  year={2025}
}