Image Segmentation
TerraTorch
English
Pytorch
mmsegmentation
segmentation
Crop Classification
Multi Temporal
Geospatial
Foundation model
Instructions to use ibm-nasa-geospatial/Prithvi-EO-1.0-100M-multi-temporal-crop-classification 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-multi-temporal-crop-classification with TerraTorch:
from terratorch.registry import BACKBONE_REGISTRY model = BACKBONE_REGISTRY.build("ibm-nasa-geospatial/Prithvi-EO-1.0-100M-multi-temporal-crop-classification") - Notebooks
- Google Colab
- Kaggle
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It is important to acknowledge that the CDL (Crop Data Layer) labels employed in this process are known to contain noise and are not entirely precise, thereby influencing the model's performance. Fine-tuning the model with more accurate labels is expected to further enhance its overall effectiveness, leading to improved results.
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### Inference
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There is an inference script that allows to run the hls-cdl crop classification model for inference on HLS images. These input have to be geotiff format, including 18 bands for 3 time-step, and each time-step includes the channels described above (Blue, Green, Red, Narrow NIR, SWIR, SWIR 2) in order.
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It is important to acknowledge that the CDL (Crop Data Layer) labels employed in this process are known to contain noise and are not entirely precise, thereby influencing the model's performance. Fine-tuning the model with more accurate labels is expected to further enhance its overall effectiveness, leading to improved results.
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### Inference
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There is an inference script that allows to run the hls-cdl crop classification model for inference on HLS images. These input have to be geotiff format, including 18 bands for 3 time-step, and each time-step includes the channels described above (Blue, Green, Red, Narrow NIR, SWIR, SWIR 2) in order.
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