  Foundation models characterized by extensive parameters and trained on
large-scale datasets have demonstrated remarkable efficacy across various
downstream tasks for remote sensing data. Current remote sensing foundation
models typically specialize in a single modality or a specific spatial
resolution range, limiting their versatility for downstream datasets. While
there have been attempts to develop multi-modal remote sensing foundation
models, they typically employ separate vision encoders for each modality or
spatial resolution, necessitating a switch in backbones contingent upon the
input data. To address this issue, we introduce a simple yet effective method,
termed OFA-Net (One-For-All Network): employing a single, shared Transformer
backbone for multiple data modalities with different spatial resolutions. Using
the masked image modeling mechanism, we pre-train a single Transformer backbone
on a curated multi-modal dataset with this simple design. Then the backbone
model can be used in different downstream tasks, thus forging a path towards a
unified foundation backbone model in Earth vision. The proposed method is
evaluated on 12 distinct downstream tasks and demonstrates promising
performance.
