Performing an inference task with TerraTorch#

Step 1: Download the test case from HuggingFace#

We will use the burn scars identification test case, in which we are interested in estimating the area affected by wildfires using a finetuned model (Prithvi-EO backbone + CNN decoder). To download the complete example, do: 

git clone https://huggingface.co/ibm-nasa-geospatial/Prithvi-EO-2.0-300M-BurnScars/

Step 2: Run the default inference case#

The example you download already contains some sample images to be used as input, so you just need to go to the local repository and create a directory to save the outputs: 

cd Prithvi-EO-2.0-300M-BurnScars
mkdir outputs
and to execute a command line like: 
terratorch predict -c burn_scars_config.yaml --predict_output_dir outputs/ --data.init_args.predict_data_root examples/ --ckpt_path Prithvi_EO_V2_300M_BurnScars.pt
You will see the outputs being saved in the outputs directory.

Input image (RGB components)#

Predicted mask#

}

More examples#

For some examples of training using the existing tasks, check out the following pages on our github repo:

From config files#

Under examples/confs

  • Flood Segmentation with ViT: sen1floods11_vit.yaml

  • Flood Segmentation with ViT and an SMP head: sen1floods11_vit_smp.yaml

  • Flood Segmentation with ViT and an MMSeg head: sen1floods11_vit_mmseg.yaml

  • Multitemporal Crop Segmentation: multitemporal_crop.yaml

  • Burn Scar Segmentation: burn_scars.yaml

  • Scene Classification: eurosat.yaml

External examples available in Prithvi-EO-2.0