This repository contains the code of the paper Trustworthy Super-Resolution of Multispectral Sentinel-2 Imagery with Latent Diffusion. In order to embed this model in your workflow, please check out SuperS2 under Section 4 - Diffusion Model , which implements many SR models including this one and provides supplementary code
pip install opensr-model# Load the model --------------------------------------------------------------
device = "cuda" if torch.cuda.is_available() else "cpu"
# set the type of model, 4x10m or 6x20m
model_type = "10m"
assert model_type in ["10m","20m"], "model_type must be either 10m or 20m"
if model_type == "10m": # if 10m, create according model and load ckpt
model = opensr_model.SRLatentDiffusion(bands=model_type,device=device) # 10m
model.load_pretrained("opensr_10m_v4_v2.ckpt") # 10m
if model_type == "20m": # if 20m, create according model and load ckpt
model = opensr_model.SRLatentDiffusion(bands=model_type,device=device) # 20m
model.load_pretrained("opensr_20m_v1.ckpt") # 20m
# set model to eval mode
model = model.eval()
# test functionality of selected model --------------------------------------------
if model_type == "10m":
X = torch.rand(1,4,128,128)
if model_type == "20m":
X = torch.rand(1,6,128,128)
sr = model(X)The model should load automatically with the moel.load_pretrained command. Alternatively, the checkpoints can be found on HuggingFace
This package contains the latent-diffusion model to super-resolute 10 and 20m bands of Sentinel-2. This repository contains the bare model. It can be embedded in the "opensr-utils" package in order to be applied to Sentinel-2 Imagery.
Some example Sr scenes can be found as super-resoluted tiffs on Doogle Drive. Scenes available:
- Buenos Aires, Argentina
- Blue Mountains, Australia
- Louisville, USA
- Kutahya, Türkyie
- Catalunya, Spain
If you use this model in your work, please cite
@ARTICLE{10887321,
author={Donike, Simon and Aybar, Cesar and Gomez-Chova, Luis and Kalaitzis, Freddie},
journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
title={Trustworthy Super-Resolution of Multispectral Sentinel-2 Imagery with Latent Diffusion},
year={2025},
volume={},
number={},
pages={1-14},
keywords={Superresolution;Remote sensing;Training;Diffusion models;Measurement;Spatial resolution;Image reconstruction;Uncertainty;Adaptation models;European Space Agency;Super-Resolution;Remote Sensing;Sentinel-2;Deep Learning;Latent Diffusion;Model Uncertainty},
doi={10.1109/JSTARS.2025.3542220}}This is a work in progress and published explicitly as a research preview. This repository will leave the experimental stage with the publication of v1.0.0.
