Eo VAE
EO-VAE is a multi-sensor variational autoencoder designed to serve as a foundational tokenizer for the Earth Observation (EO) domain. Unlike traditional approaches that require separate models for different sensors,...
Modellquelle
- Art
- Other
- Dateigröße
- 3,03 MB–1,07 GB
- Version
- vDecoder dconv weight generator init 0.01 er50k
- Aktualisiert
- 9. Okt. 2026
- Quelle
- Hugging Face
Quellenbeschreibung
EO-VAE: Towards A Multi-sensor Tokenizer for Earth Observation
EO-VAE is a multi-sensor variational autoencoder designed to serve as a foundational tokenizer for the Earth Observation (EO) domain. Unlike traditional approaches that require separate models for different sensors, EO-VAE utilizes a single model to encode and reconstruct flexible channel combinations through dynamic hypernetworks.
Model Summary
- Model Name: EO-VAE
- Paper: EO-VAE: Towards A Multi-sensor Tokenizer for Earth Observation Data
- License: Apache-2.0
- Task: Image-to-Image / Tokenization (Remote Sensing)
Usage
import torch
from eo_vae.models.new_autoencoder import EOFluxVAE
model = EOFluxVAE.from_pretrained(
repo_id="nilsleh/eo-vae",
ckpt_filename="eo-vae.ckpt",
config_filename="model_config.yaml",
device="cpu",
)
# Run reconstruction / latent extraction
x = torch.randn(1, 3, 256, 256)
# Example wavelengths for Sentinel-2 RGB
wvs = torch.tensor([0.665, 0.56, 0.49], dtype=torch.float32)
with torch.no_grad():
recon = model.reconstruct(x, wvs) # [B, 3, 256, 256]
z = model.encode_spatial_normalized(x, wvs) # [B, 32, 32, 32] for 256x256 input
These are the wavelengths used across modalities:
WAVELENGTHS = {
'S2RGB': [0.665, 0.56, 0.49],
'S1RTC': [5.4, 5.6],
'S2L2A': [
0.443, 0.490, 0.560, 0.665, 0.705, 0.740,
0.783, 0.842, 0.865, 1.610, 2.190, 0.945,
],
'S2L1C': [
0.443, 0.490, 0.560, 0.665, 0.705, 0.740,
0.783, 0.842, 0.865, 0.945, 1.375, 1.610, 2.190,
],
}
If you use this model in your work, please cite:
EO-VAE: Towards A Multi-sensor Tokenizer for Earth Observation Data
@article{eo-vae,
title={EO-VAE: Towards A Multi-sensor Tokenizer for Earth Observation Data},
author={Lehmann, Nils and Wang, Yi and Xiong, Zhitong and Zhu, Xiaoxiang},
journal={arXiv preprint arXiv:2602.12177},
year={2026}
}
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1 QuelleVerifiziert 9. Okt.
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Modellartefakte
3 Artefaktedecoder_dconv_weight_generator_init_0.01_er50k.pt
pt · 3,03 MB · SHA-256 e452546f85d7…3323 · Hugging Face
Download fileencoder_dconv_weight_generator_init_0.01_er50k.pt
pt · 3,09 MB · SHA-256 9ae67a11dc71…f651 · Hugging Face
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Quellenauszüge
2 Auszüge--- license: apache-2.0 language: - en pipeline_tag: image-to-image tags: - remote-sensing - earth-observation - vae - tokenizer --- # EO-VAE: Towards A Multi-sensor Tokenizer for Earth Observation EO-VAE is a multi-sensor variational autoencoder designed to serve as a foundational tokenizer for the Earth Observation (EO) domain. Unlike traditional approaches that require separate models for different sensors, EO-VAE utilizes a single model to encode and reconstruct flexible channel combinations through dynamic hypernetworks. ## Model Summary - **Model Name:** EO-VAE - **Paper:** [EO-VAE: Towards A Multi-sensor Tokenizer for Earth Observation Data](https://arxiv.org/abs/2602.12177) - **License:** Apache-2.0 - **Task:** Image-to-Image / Tokenization (Remote Sensing) ## Usage ```python import torch from eo_vae.models.new_autoencoder import EOFluxVAE model = EOFluxVAE.from_pretrained( repo_id="nilsleh/eo-vae", ckpt_filename="eo-vae.ckpt", config_filename="model_config.yaml", device="cpu", ) # Run reconstruction / latent extraction x = torch.randn(1, 3, 256, 256) # Example wavelengths for Sentinel-2 RGB wvs = torch.tensor([0.665, 0.56, 0.49], dtype=torch.float32) with torch.no_grad(): recon = model.reconstruct(x, wvs) # [B, 3, 256, 256] z = model.encode_spatial_normalized(x, wvs) # [B, 32, 32, 32] for 256x256 input ``` These are the wavelengths used across modalities: ```python WAVELENGTHS = { 'S2RGB': [0.665, 0.56, 0.49], 'S1RTC': [5.4, 5.6], 'S2L2A': [ 0.443, 0.490, 0.560, 0.665, 0.705, 0.740, 0.783, 0.842, 0.865, 1.610, 2.190, 0.945, ], 'S2L1C': [ 0.443, 0.490, 0.560, 0.665, 0.705, 0.740, 0.783, 0.842, 0.865, 0.945, 1.375, 1.610, 2.190, ], } ``` If you use this model in your work, please cite: [**EO-VAE: Towards A Multi-sensor Tokenizer for Earth Observation Data**](https://arxiv.org/abs/2602.12177) ```bibtex @article{eo-vae, title={EO-VAE: Towards A Multi-sensor Tokenizer for Earth Observation Data}, author={Lehmann, Nils and Wang, Yi and Xiong, Zhitong and Zhu, Xiaoxiang}, journal={arXiv preprint arXiv:2602.12177}, year={2026} } ```
Source context: 0 downloads · 1 likes · Pipeline image-to-image · Repo nilsleh/eo-vae
