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.
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.
1from transformers import AutoModel
2
3# Load the UNet-6depth-Up+Conv model
4model = AutoModel.from_pretrained("venkatesh-thiru/s2l8h-UNet-6depth-upsample", trust_remote_code=True)
5
6# Harmonize Landsat-8 images
7l8up = model(l8MS, l8pan)
Water quality assessment
Urban planning
Climate monitoring
Disaster response
Infrastructure oversight
Agricultural surveillance
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.
