Model and Inputs Prithvi EO 1.0 is a first of its kind temporal Vision transformer pre trained by the IBM and NASA team on contiguous US Harmonised Landsat Sentinel 2 (HLS) data. The model adopts a self supervised encoder developed with a ViT architecture and Masked AutoEncoder (MAE) learning strategy, with an MSE loss function. The model includes spatial attention across multiple patches and also temporal attention for each patch. The model accepts remote sensing data in a video format (B, C, T, H, W). Note that the temporal dimension (T) is very important in this application and not present in most other works around remote sensing modeling. The ability to handle a time series of remote sensing images can benefit a variety of downstream tasks (e.g. Burn Scars segmentation, Flood Segmentation, Land Cover Classification). The model can also handle static imagery which can be fed into the model with T=1. Pre training The model was pre trained with NASA's HLS V2 L30 product (30m granularity) from the contiguous United States. The bands that were used are the following: 1. Blue 2. Green 3. Red 4. Narrow NIR 5. SWIR 1 6. SWIR 2 Code The model follows the original MAE repo with some mod…
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