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README.md
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@@ -28,7 +28,7 @@ We extract the following bands for flood mapping:
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5. SWIR 1
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6. SWIR 2
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Labels represent no water (class 0), water/flood (class 1), and no data/clouds (class
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The Prithvi-100m model was initially pretrained using a sequence length of 3 timesteps. Based on the characteristics of this benchmark dataset, we focus on single-timestamp segmentation. This demonstrates that our model can be utilized with an arbitrary number of timestamps during finetuning.
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5. SWIR 1
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6. SWIR 2
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Labels represent no water (class 0), water/flood (class 1), and no data/clouds (class -1).
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The Prithvi-100m model was initially pretrained using a sequence length of 3 timesteps. Based on the characteristics of this benchmark dataset, we focus on single-timestamp segmentation. This demonstrates that our model can be utilized with an arbitrary number of timestamps during finetuning.
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