State-of-the-art generative image and video models rely heavily on tokenizers that compress high-dimensional inputs into more efficient latent representations. While this paradigm has revolutionized RGB generation, Earth observation (EO) data presents unique challenges due to diverse sensor specifications and variable spectral channels. We propose EO-VAE, a multi-sensor variational autoencoder designed to serve as a foundational tokenizer for the EO domain. Unlike prior approaches that train separate tokenizers for each modality, EO-VAE utilizes a single model to encode and reconstruct flexible channel combinations via dynamic hypernetworks. Our experiments on the TerraMesh dataset demonstrate that EO-VAE achieves superior reconstruction fidelity compared to the TerraMind tokenizers, establishing a robust baseline for latent generative modeling in remote sensing.

翻译：最先进的生成式图像与视频模型高度依赖于标记器，其将高维输入压缩为更高效的潜在表示。尽管这一范式已彻底改变了RGB图像生成领域，但地球观测数据因传感器规格多样与光谱通道可变而面临独特挑战。本文提出EO-VAE——一种专为地球观测领域设计的多传感器变分自编码器基础标记器。与先前为各模态单独训练标记器的方法不同，EO-VAE通过动态超网络，利用单一模型实现对灵活通道组合的编码与重建。在TerraMesh数据集上的实验表明，相较于TerraMind标记器，EO-VAE实现了更优的重建保真度，为遥感领域的潜在生成建模建立了稳健基准。