Galileo

Learning Global and Local Features in Pretrained Remote Sensing Models

Galileo is a family of pretrained remote sensing models. These models have been pretrained on a diversity of remote sensing inputs, and perform well on a range of benchmark tasks. For more information, please see our paper.

Using Galileo

Galileo can be loaded either from src, or from single_file_galileo.py for easy porting to other codebases:

from single_file_galileo import Encoder as SingleFileEncoder
from src.galileo import Encoder


src_model = Encoder.load_from_folder(DATA_FOLDER / "models/nano")
sf_model = SingleFileEncoder.load_from_folder(
    DATA_FOLDER / "models/nano", device=torch.device("cpu")
)

for model_p, sf_model_p in zip(src_model.parameters(), sf_model.parameters()):
    assert torch.equal(model_p, sf_model_p)

The inputs to Galileo are described in the MaskedOutput:

class MaskedOutput(NamedTuple):
    """
    A mask can take 3 values:
    0: seen by the encoder (i.e. makes the key and value tokens in the decoder)
    1: not seen by the encoder, and ignored by the decoder
    2: not seen by the encoder, and processed by the decoder (the decoder's query values)
    """

    space_time_x: torch.Tensor  # [B, H, W, T, len(SPACE_TIME_BANDS)]
    space_x: torch.Tensor  # [B, H, W, len(SPACE_BANDS)]
    time_x: torch.Tensor  # [B, T, len(TIME_BANDS)]
    static_x: torch.Tensor  # [B, len(STATIC_BANDS)]
    space_time_mask: torch.Tensor  # [B, H, W, T, len(SPACE_TIME_BANDS_GROUPS_IDX)]
    space_mask: torch.Tensor  # [B, H, W, len(SPACE_BAND_GROUPS_IDX)]
    time_mask: torch.Tensor   # [B, T, len(TIME_BAND_GROUPS_IDX)]
    static_mask: torch.Tensor  # [B, len(STATIC_BAND_GROUPS_IDX)]
    months: torch.Tensor  # [B, T]

Each of these bands are described in single_file_galileo.py.

Alternatively, a utility function is provided to transform the bands into MaskedOutput objects. This transformation is for a single instance (i.e. it omits the B dimension above). This function optionally normalizes the data against the Galileo pre-training statistics.

from src.data.utils import S2_BANDS, construct_galileo_input

t, h, w = 2, 4, 4
s2 = torch.randn((t, h, w, len(S2_BANDS)))
masked_output = construct_galileo_input(s2=s2, normalize=normalize)

If you want to see Galileo being used on real data, we also have a notebook which generates embeddings for a real training tif file.

Model weights

The nano model weights are available on github.

Other model sizes (including nano) are available on huggingface.

Reference

If you find this code useful, please cite the following paper:

@misc{tseng2025galileolearninggloballocal,
      title={Galileo: Learning Global and Local Features in Pretrained Remote Sensing Models},
      author={Gabriel Tseng and Anthony Fuller and Marlena Reil and Henry Herzog and Patrick Beukema and Favyen Bastani and James R. Green and Evan Shelhamer and Hannah Kerner and David Rolnick},
      year={2025},
      eprint={2502.09356},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2502.09356},
}