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README.md
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@@ -27,7 +27,7 @@ An example of fine-tuning the model for image segmentation using Terratorch for
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The granite-geospatial-uki model uses the [Prithvi-EO](https://huggingface.co/collections/ibm-nasa-geospatial/prithvi-for-earth-observation-6740a7a81883466bf41d93d6)
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model architecture. It was pre-trained on HLS data for the continental USA, followed by additional pre-training using 16,000 HLS L30 and
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Sentinel-1 images covering the United Kingdom and Ireland. The Sentinel-1 SAR backscatter
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as the HLS data, and were normalized by \\(10log(\sigma_0)\\), where pixels with \\(10log(\sigma_0) > 10\\) are set to \\(10\\) and \\(10log(\sigma_0) < -35\\)
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are set to \\(-35\\). The two additional Sentinel-1 bands were initialized with the mean weights of the other channels for pre-training.
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The following bands were used in the pre-trained model:
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The granite-geospatial-uki model uses the [Prithvi-EO](https://huggingface.co/collections/ibm-nasa-geospatial/prithvi-for-earth-observation-6740a7a81883466bf41d93d6)
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model architecture. It was pre-trained on HLS data for the continental USA, followed by additional pre-training using 16,000 HLS L30 and
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Sentinel-1 images covering the United Kingdom and Ireland. The Sentinel-1 SAR backscatter \\(\sigma_0\\) were resampled to the same resolution
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as the HLS data, and were normalized by \\(10log(\sigma_0)\\), where pixels with \\(10log(\sigma_0) > 10\\) are set to \\(10\\) and \\(10log(\sigma_0) < -35\\)
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are set to \\(-35\\). The two additional Sentinel-1 bands were initialized with the mean weights of the other channels for pre-training.
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The following bands were used in the pre-trained model:
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