Image Segmentation
TerraTorch
English
Pytorch
mmsegmentation
segmentation
burn scars
Geospatial
Foundation model
Instructions to use ibm-nasa-geospatial/Prithvi-EO-1.0-100M-burn-scar 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-burn-scar with TerraTorch:
from terratorch.registry import BACKBONE_REGISTRY model = BACKBONE_REGISTRY.build("ibm-nasa-geospatial/Prithvi-EO-1.0-100M-burn-scar") - Notebooks
- Google Colab
- Kaggle
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README.md
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The github repo includes an inference script that allows to run the burn scar model for inference on HLS images. These input have to be geotiff format, including the channels described above (Blue, Green, Red, Narrow NIR, SWIR, SWIR 2) in reflectance units [0-1]. There is also a **demo** that leverages the same code **[here](https://huggingface.co/spaces/ibm-nasa-geospatial/Prithvi-100M-Burn-scars-demo)**.
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### Citation
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## Citation
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If this model helped your research, please cite `Prithvi-100M-burn-scar` in your publications. Here is an example BibTeX entry:
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The github repo includes an inference script that allows to run the burn scar model for inference on HLS images. These input have to be geotiff format, including the channels described above (Blue, Green, Red, Narrow NIR, SWIR, SWIR 2) in reflectance units [0-1]. There is also a **demo** that leverages the same code **[here](https://huggingface.co/spaces/ibm-nasa-geospatial/Prithvi-100M-Burn-scars-demo)**.
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### Citation
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If this model helped your research, please cite `Prithvi-100M-burn-scar` in your publications. Here is an example BibTeX entry:
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