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--- |
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license: mit |
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tags: |
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- agriculture |
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- remote sensing |
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- earth observation |
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- landsat |
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- sentinel-2 |
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--- |
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## Model Card for UNet-6depth-Up+Conv: `venkatesh-thiru/s2l8h-UNet-6depth-upsample` |
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### Model Description |
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The UNet-6depth-upsample model is designed to harmonize Landsat-8 and Sentinel-2 satellite imagery by enhancing the spatial resolution of Landsat-8 images. This model takes in Landsat-8 multispectral images (Bottom of the Atmosphere (L2) Reflectances) and pan-chromatic images (Top of the Atmosphere (L1) Reflectances) and outputs images that match the spectral and spatial qualities of Sentinel-2 data. |
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### Model Architecture |
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This model is a UNet architecture with 6 depth levels and utilizes upsampling combined with convolutional layers to achieve high-fidelity image enhancement. The depth and convolutional layers are fine-tuned to provide a robust transformation that ensures improved spatial resolution and spectral consistency with Sentinel-2 images. |
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### Usage |
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```python |
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from transformers import AutoModel |
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# Load the UNet-6depth-Up+Conv model |
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model = AutoModel.from_pretrained("venkatesh-thiru/s2l8h-UNet-6depth-upsample", trust_remote_code=True) |
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# Harmonize Landsat-8 images |
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l8up = model(l8MS, l8pan) |
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``` |
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Where: |
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`l8MS` - Landsat Multispectral images (L2 Reflectances) |
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`l8pan` - Landsat Pan-Chromatic images (L1 Reflectances) |
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### Applications |
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Water quality assessment |
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Urban planning |
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Climate monitoring |
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Disaster response |
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Infrastructure oversight |
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Agricultural surveillance |
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### Limitations |
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While the model generalizes well to most regions of the world, minor limitations may occur in areas with significantly different spectral characteristics or extreme environmental conditions. |
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### Reference |
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For more details, refer to the publication: 10.1016/j.isprsjprs.2024.04.026 |
