Update README.md

README.md

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----
-license: mit
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+---
+license: mit
+tags:
+- agriculture
+- remote sensing
+- earth observation
+- landsat
+- sentinel-2
+---
+## Model Card for UNet-6depth-Up+Conv: `venkatesh-thiru/s2l8h-UNet-6depth-upsample`
+
+### Model Description
+
+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.
+
+### Model Architecture
+
+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.
+
+### Usage
+
+```python
+from transformers import AutoModel
+
+# Load the UNet-6depth-Up+Conv model
+model = AutoModel.from_pretrained("venkatesh-thiru/s2l8h-UNet-6depth-upsample", trust_remote_code=True)
+
+# Harmonize Landsat-8 images
+l8up = model(l8MS, l8pan)
+```
+
+Where:
+
+`l8MS` - Landsat Multispectral images (L2 Reflectances)
+
+`l8pan` - Landsat Pan-Chromatic images (L1 Reflectances)
+
+### Applications
+Water quality assessment
+Urban planning
+Climate monitoring
+Disaster response
+Infrastructure oversight
+Agricultural surveillance
+
+### Limitations
+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.
+
+### Reference
+For more details, refer to the publication: 10.1016/j.isprsjprs.2024.04.026