--- license: mit --- # Galileo Learning Global and Local Features in Pretrained Remote Sensing Models Galileo_diagram 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: ```python 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`](src/masking.py#L116): ```python 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`](single_file_galileo.py#L24). Alternatively, a [utility function](src/data/utils.py#L36) 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. ```python 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) ```