Datasets:
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README.md
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license: cc-by-3.0
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---
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## WorldFloods Phisat-2 dataset
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Phisat-2 like images were simulated using the code in the [OrbitalAI Challenge](https://github.com/AI4EO/orbitalAI). For storing the dataset, we followed the [PhiSatNet repo](https://github.com/sirbastiano/PhiSatNet) Data Specification Format.
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Since the WorldFloodsv2 dataset contains full flood scenes rather than chips, we created non-overlapping Train and Validation patches of size 512 from the simulated images, and only stored patches with less than 20% cloud cover, as we do on the fly to train the flood segmentation models described in the paper [Global flood extent segmentation in optical satellite images](https://www.nature.com/articles/s41598-023-47595-7).
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For Test samples, however, we kept the full scenes as this is a good practice when evaluating flood detection models.
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The total size of the dataset is 389 GB.
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license: cc-by-3.0
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task_categories:
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- image-segmentation
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language:
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- en
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tags:
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- floods
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- earth-observation
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- deep-learnig
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- Phisat-2
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pretty_name: WorldFloods-Phisat-2
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size_categories:
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- 10K<n<100K
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---
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## WorldFloods Phisat-2 dataset
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Phisat-2 like images were simulated using the code in the [OrbitalAI Challenge](https://github.com/AI4EO/orbitalAI). For storing the dataset, we followed the [PhiSatNet repo](https://github.com/sirbastiano/PhiSatNet) Data Specification Format.
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Since the WorldFloodsv2 dataset contains full flood scenes rather than chips, we created non-overlapping Train and Validation patches of size 512 from the simulated images, and only stored patches with less than 20% cloud cover, as we do on the fly to train the flood segmentation models described in the paper [Global flood extent segmentation in optical satellite images](https://www.nature.com/articles/s41598-023-47595-7).
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For Test samples, however, we kept the full scenes as this is a good practice when evaluating flood detection models.
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The total size of the dataset is 389 GB.
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