---
license:
  - cc0-1.0
language:
- en
tags:
  - sentinel-2
  - super-resolution
  - harmonization
  - synthetic
  - cross-sensor
  - temporal
pretty_name: sen2naipv2
---

<center>
<img src='images/logo.png' alt='drawing' width='20%'/>
</center>


# sen2naipv2

****A large-scale dataset for Sentinel-2 Image Super-Resolution****


The SEN2NAIPv2 dataset is an extension of [SEN2NAIP](https://huggingface.co/datasets/isp-uv-es/SEN2NAIP), 
containing 62,242 LR and HR image pairs, about 76% more images than the first version. The dataset files
are named `sen2naipv2-unet-000{1..3}.part.taco`. This dataset comprises synthetic RGBN NAIP bands at 2.5 and 10 meters,
degraded to corresponding Sentinel-2 images and a potential x4 factor. The degradation model to generate 
the LR pair comprises three sequential steps: (1) Gaussian blurring and bilinear downsampling, (2) reflectance
harmonization, and (3) adding noise. Reflectance harmonization is the most critical of these steps. In version
1, the harmonization model used a U-Net architecture to convert Gaussian-blurred NAIP images into 
reflectance-correct Sentinel-2-like imagery. This initial U-Net model was trained on just 2,851 % same-day 
Sentinel-2 and NAIP imagery. In version 2, the U-Net model was retrained. The temporal threshold was expanded 
from one day to a 3-day range, and the search included the full Sentinel-2 archive available for the 
USA, increasing the cross-sensor dataset size to 35,217 images. The kernel degradation and noise model 
components remain consistent between the two versions.
In addition to the synthetic dataset (`sen2naipv2-unet`), two new variants are introduced in 
SEN2NAIPv2:
1. **sen2naipv2-histmatch:** Identical to `sen2naipv2-unet` but uses histogram matching instead of 
style transfer for reflectance harmonization using the closest Sentinel-2 image. We report the
time difference between the NAIP and Sentinel-2 images used for harmonization.
2. **sen2naipv2-crosssensor:** –  In contrast to synthetic SEN2NAIPv2 datasets, the cross-sensor 
SEN2NAIPv2 dataset is comparatively smaller, comprising only images captured within a one-day timeframe
between Sentinel-2 and NAIP. To guarantee that the Sentinel-2 images are cloud-free, we exclude any 
images with a cloud cover greater than 0 % as reported by the [UnetMob-V2 cloud detector](https://cloudsen12.github.io/).
To ensure the reliability of the dataset, we first calculated the Pearson correlation in 16 x 16 kernels 
between the Sentinel-2 images and a Sentinel-2-like version (see correlation field) obtained by degrading NAIP imagery. The 
degradation process was similar to the one described in the synthetic SEN2NAIPv2 paper but employed histogram matching 
instead of the U-Net-style transfer model, as a reliable LR reference exists. In addition, we applied a hard 
constraint to the HR images using as a reference the real Sentinel-2 to further improve harmonization.
3. **sen2naipv2-temporal:** A temporal variant of the SEN2NAIPv2 dataset, where the LR and HR image pairs are
generated synthetically using the same degradation model as the `sen2naipv2-unet` dataset.


<center>
<img src='images/map.png' alt='drawing' width='75%'/>
<sup>
  The spatial coverage of the datasets `sen2naipv2-histmatch` and `sen2naipv2-unet` is illustrated. The low-resolution (LR) patches 
  measure 130 × 130 pixels, while the high-resolution (HR) patches measure 520 × 520 pixels. Blue stars indicate the spatial locations 
  of the cross-sensor subset.
  </sup>
</center>


## 🌮 TACO Snippet


Load this dataset using the `tacoreader` library.
```python
import tacoreader
import rasterio as rio

# Load the Cloud-Optimized Dataset
file = "https://huggingface.co/datasets/tacofoundation/tortilla_demo/resolve/main/sen2naipv2_real.tortilla"
dataset = tacoreader.load(file)

# Read a sample
sample_idx = 2000
lr = dataset.read(sample_idx).read(0)
hr = dataset.read(sample_idx).read(1)

# Retrieve the data
with rio.open(lr) as src, rio.open(hr) as dst:
    lr_data = src.read(window=rio.windows.Window(0, 0, 256//4, 256//4))
    hr_data = dst.read(window=rio.windows.Window(0, 0, 256, 256))
```

Or in R:

```r
library(tacoreader)
file <- "https://huggingface.co/datasets/tacofoundation/tortilla_demo/resolve/main/sen2naipv2_real.tortilla"
dataset <- tacoreader::load(file)
```
## 🛰️ Sensor Information


The sensor related to the dataset: **sentinel2msi**
## 🎯 Task


The task associated with this dataset: **super-resolution**
## 📂 Original Data Repository


Source location of the raw data:**[https://huggingface.co/datasets/isp-uv-es/SEN2NAIP](https://huggingface.co/datasets/isp-uv-es/SEN2NAIP)**
## 💬 Discussion


Insights or clarifications about the dataset: **[https://huggingface.co/datasets/tacofoundation/sen2naipv2/discussions](https://huggingface.co/datasets/tacofoundation/sen2naipv2/discussions)**
## 🔀 Split Strategy


How the dataset is divided for training, validation, and testing: **stratified**
## 📚 Scientific Publications


Publications that reference or describe the dataset.

### Publication 01
- **DOI**: [10.1109/LGRS.2024.3401394](10.1109/LGRS.2024.3401394)
- **Summary**: Set of tools to evaluate super-resolution models in the context of Sentinel-2 imagery.
- **BibTeX Citation**:
```bibtex
@article{aybar2024comprehensive,
  title={A Comprehensive Benchmark for Optical Remote Sensing Image Super-Resolution},
  author={Aybar, Cesar and Montero, David and Donike, Simon and Kalaitzis, Freddie and G{'o}mez-Chova, Luis},
  journal={IEEE Geoscience and Remote Sensing Letters},
  year={2024},
  publisher={IEEE}
}
```



### Publication 02
- **DOI**: [10.1109/LGRS.2024.3401394](10.1109/LGRS.2024.3401394)
- **Summary**: Version 1 of the SEN2NAIPv2 dataset.
- **BibTeX Citation**:
```bibtex
@article{aybar2024sen2naip,
  title={SEN2NAIP \: A large-scale dataset for Sentinel-2 Image Super-Resolution},
  author={Aybar, Cesar and Montero, David and Donike, Simon and Kalaitzis, Freddie and G{'o}mez-Chova, Luis},
  journal={Scientific Data},
  volume={9},
  number={1},
  pages={782},
  year={2022},
  publisher={Nature Publishing Group UK London}
}
```


## 🤝 Data Providers


Organizations or individuals responsible for the dataset.
|**Name**|**Role**|**URL**|
| :--- | :--- | :--- |
|Image & Signal Processing|host|[https://isp.uv.es/](https://isp.uv.es/)|
|USDA Farm Production and Conservation - Business Center, Geospatial Enterprise Operations|producer|[https://www.fpacbc.usda.gov/](https://www.fpacbc.usda.gov/)|
|European Space Agency|producer|[https://www.esa.int/](https://www.esa.int/)|

## 🧑‍🔬 Curators


Responsible for structuring the dataset in the TACO format.
|**Name**|**Organization**|**URL**|
| :--- | :--- | :--- |
|Cesar Aybar|Image & Signal Processing|[https://csaybar.github.io/](https://csaybar.github.io/)|

## 🌈 Optical Bands


Spectral bands related to the sensor.
|**Name**|**Common Name**|**Description**|**Center Wavelength**|**Full Width Half Max**|**Index**|
| :--- | :--- | :--- | :--- | :--- | :--- |
|B02|blue|Band 2 - Blue - 10m|496.5|53.0|0|
|B03|green|Band 3 - Green - 10m|560.0|34.0|1|
|B04|red|Band 4 - Red - 10m|664.5|29.0|2|
|B08|NIR|Band 8 - Near infrared - 10m|840.0|114.0|3|