Prithvi-EO-1.0-100M
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Prithvi-EO-2.0-100M-TL
ibm-nasa-geospatialPrithvi-EO-2.0 is the second generation EO foundation model jointly developed by IBM, NASA, and Jülich Supercomputing Centre. Prithvi-EO-2.0 is based on the ViT architecture, pretrained using a masked autoencoder (MAE) approach, with two major modifications as shown in the figure below. First, we replaced the 2D patch embeddings and 2D positional embeddings with 3D versions to support inputs with spatiotemporal characteristics, i.e., a sequence of T images of size (H, W). Our 3D patch embeddings consist of a 3D convolutional layer, dividing the 3D input into non-overlapping cubes of size (t, h, w) for time, height, and width dimensions, respectively. For the 3D positional encodings, we first generate 1D sin/cos encodings individually for each dimension and then combine them together into a single, 3D positional encoding.
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View details ↗Prithvi-EO-2.0-300M
ibm-nasa-geospatialPrithvi-EO-2.0 is the second generation EO foundation model jointly developed by IBM, NASA, and Jülich Supercomputing Centre. Prithvi-EO-2.0 is based on the ViT architecture, pretrained using a masked autoencoder (MAE) approach, with two major modifications as shown in the figure below. First, we replaced the 2D patch embeddings and 2D positional embeddings with 3D versions to support inputs with spatiotemporal characteristics, i.e., a sequence of T images of size (H, W). Our 3D patch embeddings consist of a 3D convolutional layer, dividing the 3D input into non-overlapping cubes of size (t, h, w) for time, height, and width dimensions, respectively. For the 3D positional encodings, we first generate 1D sin/cos encodings individually for each dimension and then combine them together into a single, 3D positional encoding.
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View details ↗Prithvi-EO-2.0-300M-BurnScars
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View details ↗Prithvi-EO-2.0-300M-TL
ibm-nasa-geospatialPrithvi-EO-2.0 is the second generation EO foundation model jointly developed by IBM, NASA, and Jülich Supercomputing Centre. Prithvi-EO-2.0 is based on the ViT architecture, pretrained using a masked autoencoder (MAE) approach, with two major modifications as shown in the figure below. First, we replaced the 2D patch embeddings and 2D positional embeddings with 3D versions to support inputs with spatiotemporal characteristics, i.e., a sequence of T images of size (H, W). Our 3D patch embeddings consist of a 3D convolutional layer, dividing the 3D input into non-overlapping cubes of size (t, h, w) for time, height, and width dimensions, respectively. For the 3D positional encodings, we first generate 1D sin/cos encodings individually for each dimension and then combine them together into a single, 3D positional encoding.
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View details ↗Prithvi-EO-2.0-300M-TL-Sen1Floods11
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View details ↗Prithvi-EO-2.0-600M
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View details ↗Prithvi-EO-2.0-600M-TL
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View details ↗Prithvi-EO-2.0-tiny-TL
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View details ↗Prithvi-WxC-1.0-2300M
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View details ↗Prithvi-WxC-1.0-2300m-gravity-wave-parameterization
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