Image Segmentation
TerraTorch
English
Pytorch
mmsegmentation
segmentation
Flood mapping
Sentinel-2
Geospatial
Foundation model
Instructions to use ibm-nasa-geospatial/Prithvi-EO-1.0-100M-sen1floods11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TerraTorch
How to use ibm-nasa-geospatial/Prithvi-EO-1.0-100M-sen1floods11 with TerraTorch:
from terratorch.registry import BACKBONE_REGISTRY model = BACKBONE_REGISTRY.build("ibm-nasa-geospatial/Prithvi-EO-1.0-100M-sen1floods11") - Notebooks
- Google Colab
- Kaggle
Correct a typo: "extend of floods" corrected to "extent of floods"
Browse files
README.md
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- IoU
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### Model and Inputs
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The pretrained [Prithvi-100m](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M/blob/main/README.md) model is finetuned to segment the
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The dataset consists of 446 labeled 512x512 chips that span all 14 biomes, 357 ecoregions, and 6 continents of the world across 11 flood events. The benchmark associated to Sen1Floods11 provides results for fully convolutional neural networks trained in various input/labeled data setups, considering Sentinel-1 and Sentinel-2 imagery.
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- IoU
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### Model and Inputs
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The pretrained [Prithvi-100m](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M/blob/main/README.md) model is finetuned to segment the extent of floods on Sentinel-2 images from the [Sen1Floods11 dataset](https://github.com/cloudtostreet/Sen1Floods11).
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The dataset consists of 446 labeled 512x512 chips that span all 14 biomes, 357 ecoregions, and 6 continents of the world across 11 flood events. The benchmark associated to Sen1Floods11 provides results for fully convolutional neural networks trained in various input/labeled data setups, considering Sentinel-1 and Sentinel-2 imagery.
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