| license: apache-2.0 | |
| language: | |
| - en | |
| tags: | |
| - Pytorch | |
| - mmsegmentation | |
| - segmentation | |
| - Crop Classification | |
| - Multi Temporal | |
| - Geospatial | |
| - Foundation model | |
| datasets: | |
| - ibm-nasa-geospatial/hls_burn_scars | |
| metrics: | |
| - accuracy | |
| - IoU | |
| ### Model and Inputs | |
| The pretrained [Prithvi-100m](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M/blob/main/README.md) parameter model is finetuned to classify crop and other land cover types based off HLS data and CDL labels from the [HLS Burn Scar Scenes dataset](https://huggingface.co/datasets/ibm-nasa-geospatial/hls_burn_scars). | |
| 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 | |
| While the Prithvi-100m was pretrained with 3 timesteps, this task utilize the capibility of multi-temporal data input adapted from the pretrained foundation model and provide more generalized and | |
| ### Code | |
| Code for Finetuning is available through [github](https://github.com/NASA-IMPACT/hls-foundation-os/tree/main/fine-tuning-examples) | |
| Configuration used for finetuning is available through [config](https://github.com/NASA-IMPACT/hls-foundation-os/blob/main/fine-tuning-examples/configs/firescars_config.py | |
| ). | |
| ### Results | |
| The experiment by running the mmseg stack for 80 epochs using the above config led to an IoU of **0.72** on the burn scar class and **0.96** overall accuracy. It is noteworthy that this leads to a resonably good model, but further developement will most likely improve performance. | |
| ### Inference and demo | |
| 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. There is also a **demo** that leverages the same code **[here](https://huggingface.co/spaces/ibm-nasa-geospatial/Prithvi-100M-Burn-scars-demo)**. | |