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Model and Inputs

The pretrained Prithvi-100m parameter model is finetuned to classify crop and other land cover types based off HLS data and CDL labels from the multi_temporal_crop_classification dataset.

This dataset includes input chips of 224x224x18, where 224 is the height and width and 18 is combined with 6 bands of 3 time-steps. The bands are:

  1. Blue
  2. Green
  3. Red
  4. Narrow NIR
  5. SWIR 1
  6. SWIR 2

Labels are from CDL(Crop Data Layer) and classified into 13 classes.

The Prithvi-100m model was initially pretrained using a sequence length of 3 timesteps. For this task, we leverage the capacity for multi-temporal data input, which has been integrated from the foundational pretrained model. This adaptation allows us to achieve more generalized finetuning outcomes.


Code for Finetuning is available through github

Configuration used for finetuning is available through config.


The experiment by running the mmseg stack for 80 epochs using the above config led to the following result:

Classes IoU Acc
Natural Vegetation 0.4038 46.89%
Forest 0.4747 66.38%
Corn 0.5491 65.47%
Soybeans 0.5297 67.46%
Wetlands 0.402 58.91%
Developed/Barren 0.3611 56.49%
Open Water 0.6804 90.37%
Winter Wheat 0.4967 67.16%
Alfalfa 0.3084 66.75%
Fallow/Idle Cropland 0.3493 59.23%
Cotton 0.3237 66.94%
Sorghum 0.3283 73.56%
Other 0.3427 47.12%
aAcc mIoU mAcc
60.64% 0.4269 64.06%

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.


The baseline model along with its results can be accessed here.


The github repo includes 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. There is also a demo that leverages the same code here.


Your feedback is invaluable to us. If you have any feedback about the model, please feel free to share it with us. You can do this by submitting issues on our open-source repository, hls-foundation-os, on GitHub.


If this model helped your research, please cite HLS Multi Temporal Crop Classification Model in your publications. Here is an example BibTeX entry:

    author = {Li, Hanxi (Steve) and Khallaghi, Sam and Cecil, Michael and Kordi, Fatemeh and Fraccaro, Paolo and Alemohammad, Hamed and Ramachandran, Rahul},
    doi    = { 10.57967/hf/0954 },
    month  = aug,
    title  = {{HLS Multi Temporal Crop Classification Model}},
    url    = {https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M-multi-temporal-crop-classification},
    year   = {2023}
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