---
license: mit
---

## Model Card for UNet-6depth-shuffle: `venkatesh-thiru/s2l8h-UNet-6depth-shuffle`

### Model Description

The UNet-6depth-shuffle model harmonizes Landsat-8 and Sentinel-2 imagery by improving the spatial resolution of Landsat-8 images. This model uses Landsat-8 multispectral and pan-chromatic images to produce outputs that match the Sentinel-2's spectral and spatial characteristics.

### Model Architecture

This UNet model features 6 depth levels and incorporates a shuffling mechanism to enhance image resolution and spectral accuracy. The depth and shuffling operations are tailored to achieve high-quality transformations, ensuring the output images closely resemble Sentinel-2 data.

### Usage

```python
from transformers import AutoModel

# Load the UNet-6depth-shuffle model
model = AutoModel.from_pretrained("venkatesh-thiru/s2l8h-UNet-6depth-shuffle", 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
Minor limitations may arise in regions with different spectral properties or under extreme environmental conditions.

### Reference
For more details, refer to the publication: 10.1016/j.isprsjprs.2024.04.026