---
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