SEN2NAIPv2

---
license:
  - cc0-1.0
language:
- en
tags:
  - sentinel-2
  - super-resolution
  - harmonization
  - synthetic
  - cross-sensor
  - temporal
pretty_name: sen2naipv2
viewer: false
---
<div style="text-align: center; border: 1px solid #ddd; border-radius: 10px; padding: 15px; max-width: 250px; margin: auto; background-color: #f9f9f9;">

  ![Dataset Image](taco.png)
  
<b><p>This dataset follows the TACO specification.</p></b>
</div>


# 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 2-day range, and the search included the full Sentinel-2 archive available for the 
USA, increasing the cross-sensor dataset size to 34,640 images. The kernel degradation and noise model 
components remain consistent between the two versions.

In addition to the synthetic dataset (`sen2naipv2-unet`), three new variants are introduced in 
SEN2NAIPv2:

1. **`sen2naipv2-histmatch:`** (61282 samples) - 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:`** (8000 samples) – This variant of the SEN2NAIPv2 dataset is smaller than
its synthetic counterparts and includes only **real Sentinel-2**. The dataset is restricted to those captured within
**a one-day interval** between Sentinel-2 and NAIP sensors. To ensure cloud-free Sentinel-2 images, any with
cloud cover exceeding 0 \%, as determined by the [UnetMob-V2 cloud detector](https://cloudsen12.github.io/),
were excluded. The dataset reports the 2nd percentile of Pearson correlations calculated within 16x16 kernels
(see `correlation` field) between Sentinel-2 images and a Sentinel-2-like version derived from
degraded NAIP imagery. This degradation followed a process similar to the **`sen2naipv2-histmatch`**. This
metric provides insight into the quality of the match between Sentinel-2 and the low-frequency components
of NAIP. Additionally, a strict constraint was applied to the high-resolution images, using real Sentinel-2 data as
a reference to further enhance harmonization.

3. **`sen2naipv2-temporal`:** A temporal variant of the SEN2NAIPv2 dataset, where the LR are real Sentinel-2 
ages and the HR image has been normalized with the closest Sentinel-2 images using only histogram matching.
The temporal LR sequences **always** consist of 16 images, with the nearest image captured **always** within
0–10 days.


<center>
<img src='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>


## 🔄 Reproducible Example

<a target="_blank" href="https://colab.research.google.com/drive/1HpirWWZvcZlS2LU9uGc1yIzG04Cu1L33">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>


Load this dataset using the `tacoreader` library.

```python
import tacoreader
import rasterio as rio
print(tacoreader.__version__) # 0.4.5
# Remotely load the Cloud-Optimized Dataset 
dataset = tacoreader.load("tacofoundation:sen2naipv2-unet")
#dataset = tacoreader.load("tacofoundation:sen2naipv2-crosssensor")
#dataset = tacoreader.load("tacofoundation:sen2naipv2-histmatch")
#dataset = tacoreader.load("tacofoundation:sen2naipv2-temporal")
# Read a sample
sample_idx = 4000
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))
# Display
fig, ax = plt.subplots(1, 2, figsize=(10, 5))
ax[0].imshow(lr_data.transpose(1, 2, 0) / 3000)
ax[0].set_title("Low Resolution - Sentinel 2")
ax[1].imshow(hr_data.transpose(1, 2, 0) / 3000)
ax[1].set_title("High Resolution - NAIP")
plt.show()
```

**MORE EXAMPLES IN THE PREVIOUS COLAB**

<center>
<img src='https://cdn-uploads.huggingface.co/production/uploads/6402474cfa1acad600659e92/0QDq0EttQxwF6f-VCLrIo.png' alt='drawing' width='70%'/>
</center>


## 🛰️ 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**: [10.1038/s41597-024-04214-y](https://doi.org/10.1038/s41597-024-04214-y)
- **Summary**: Version 1 of the SEN2NAIPv2 dataset.
- **BibTeX Citation**:
```bibtex
@article{aybar2025sen2naipv2,
  author    = {Aybar, Cesar and Montero, David and Contreras, Julio and Donike, Simon and Kalaitzis, Freddie and Gómez-Chova, Luis},
  title     = {SEN2NAIP: A large-scale dataset for Sentinel-2 Image Super-Resolution},
  journal   = {Scientific Data},
  year      = {2024},
  volume    = {11},
  number    = {1},
  pages     = {1389},
  doi       = {10.1038/s41597-024-04214-y},
  url       = {https://doi.org/10.1038/s41597-024-04214-y},
  abstract  = {The increasing demand for high spatial resolution in remote sensing has underscored the need for super-resolution (SR) algorithms that can upscale low-resolution (LR) images to high-resolution (HR) ones. To address this, we present SEN2NAIP, a novel and extensive dataset explicitly developed to support SR model training. SEN2NAIP comprises two main components. The first is a set of 2,851 LR-HR image pairs, each covering 1.46 square kilometers. These pairs are produced using LR images from Sentinel-2 (S2) and corresponding HR images from the National Agriculture Imagery Program (NAIP). Using this cross-sensor dataset, we developed a degradation model capable of converting NAIP images to match the characteristics of S2 imagery ($S_{2-like}$). This led to the creation of a second subset, consisting of 35,314 NAIP images and their corresponding $S_{2-like}$ counterparts, generated using the degradation model. With the SEN2NAIP dataset, we aim to provide a valuable resource that facilitates the exploration of new techniques for enhancing the spatial resolution of Sentinel-2 imagery.},
  issn      = {2052-4463}
}
```

### Publication 02
- **DOI**: [10.1109/LGRS.2024.3401394](https://doi.org/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}
}
```

## 🤝 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**|
| :--- | :--- | :--- | :--- | :--- | :--- |
|B04|red|Band 4 - Red - 10m|664.5|29.0|0|
|B03|green|Band 3 - Green - 10m|560.0|34.0|1|
|B02|blue|Band 2 - Blue - 10m|496.5|53.0|2|
|B08|NIR|Band 8 - Near infrared - 10m|840.0|114.0|3|

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

	<div style="text-align: center; border: 1px solid #ddd; border-radius: 10px; padding: 15px; max-width: 250px; margin: auto; background-color: #f9f9f9;">

	![Dataset Image](taco.png)

	<b><p>This dataset follows the TACO specification.</p></b>
	</div>


	# 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 2-day range, and the search included the full Sentinel-2 archive available for the
	USA, increasing the cross-sensor dataset size to 34,640 images. The kernel degradation and noise model
	components remain consistent between the two versions.

	In addition to the synthetic dataset (`sen2naipv2-unet`), three new variants are introduced in
	SEN2NAIPv2:

	1. `sen2naipv2-histmatch:` (61282 samples) - 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:` (8000 samples) – This variant of the SEN2NAIPv2 dataset is smaller than
	its synthetic counterparts and includes only real Sentinel-2. The dataset is restricted to those captured within
	a one-day interval between Sentinel-2 and NAIP sensors. To ensure cloud-free Sentinel-2 images, any with
	cloud cover exceeding 0 \%, as determined by the [UnetMob-V2 cloud detector](https://cloudsen12.github.io/),
	were excluded. The dataset reports the 2nd percentile of Pearson correlations calculated within 16x16 kernels
	(see `correlation` field) between Sentinel-2 images and a Sentinel-2-like version derived from
	degraded NAIP imagery. This degradation followed a process similar to the `sen2naipv2-histmatch`. This
	metric provides insight into the quality of the match between Sentinel-2 and the low-frequency components
	of NAIP. Additionally, a strict constraint was applied to the high-resolution images, using real Sentinel-2 data as
	a reference to further enhance harmonization.

	3. `sen2naipv2-temporal`: A temporal variant of the SEN2NAIPv2 dataset, where the LR are real Sentinel-2
	ages and the HR image has been normalized with the closest Sentinel-2 images using only histogram matching.
	The temporal LR sequences always consist of 16 images, with the nearest image captured always within
	0–10 days.


	<center>
	<img src='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>


	## 🔄 Reproducible Example

	<a target="_blank" href="https://colab.research.google.com/drive/1HpirWWZvcZlS2LU9uGc1yIzG04Cu1L33">
	<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
	</a>


	Load this dataset using the `tacoreader` library.

	```python
	import tacoreader
	import rasterio as rio

	print(tacoreader.__version__) # 0.4.5

	# Remotely load the Cloud-Optimized Dataset
	dataset = tacoreader.load("tacofoundation:sen2naipv2-unet")
	#dataset = tacoreader.load("tacofoundation:sen2naipv2-crosssensor")
	#dataset = tacoreader.load("tacofoundation:sen2naipv2-histmatch")
	#dataset = tacoreader.load("tacofoundation:sen2naipv2-temporal")


	# Read a sample
	sample_idx = 4000
	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))

	# Display
	fig, ax = plt.subplots(1, 2, figsize=(10, 5))
	ax[0].imshow(lr_data.transpose(1, 2, 0) / 3000)
	ax[0].set_title("Low Resolution - Sentinel 2")
	ax[1].imshow(hr_data.transpose(1, 2, 0) / 3000)
	ax[1].set_title("High Resolution - NAIP")
	plt.show()
	```

	MORE EXAMPLES IN THE PREVIOUS COLAB

	<center>
	<img src='https://cdn-uploads.huggingface.co/production/uploads/6402474cfa1acad600659e92/0QDq0EttQxwF6f-VCLrIo.png' alt='drawing' width='70%'/>
	</center>


	## 🛰️ 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: [10.1038/s41597-024-04214-y](https://doi.org/10.1038/s41597-024-04214-y)
	- Summary: Version 1 of the SEN2NAIPv2 dataset.
	- BibTeX Citation:
	```bibtex
	@article{aybar2025sen2naipv2,
	author = {Aybar, Cesar and Montero, David and Contreras, Julio and Donike, Simon and Kalaitzis, Freddie and Gómez-Chova, Luis},
	title = {SEN2NAIP: A large-scale dataset for Sentinel-2 Image Super-Resolution},
	journal = {Scientific Data},
	year = {2024},
	volume = {11},
	number = {1},
	pages = {1389},
	doi = {10.1038/s41597-024-04214-y},
	url = {https://doi.org/10.1038/s41597-024-04214-y},
	abstract = {The increasing demand for high spatial resolution in remote sensing has underscored the need for super-resolution (SR) algorithms that can upscale low-resolution (LR) images to high-resolution (HR) ones. To address this, we present SEN2NAIP, a novel and extensive dataset explicitly developed to support SR model training. SEN2NAIP comprises two main components. The first is a set of 2,851 LR-HR image pairs, each covering 1.46 square kilometers. These pairs are produced using LR images from Sentinel-2 (S2) and corresponding HR images from the National Agriculture Imagery Program (NAIP). Using this cross-sensor dataset, we developed a degradation model capable of converting NAIP images to match the characteristics of S2 imagery ($S_{2-like}$). This led to the creation of a second subset, consisting of 35,314 NAIP images and their corresponding $S_{2-like}$ counterparts, generated using the degradation model. With the SEN2NAIP dataset, we aim to provide a valuable resource that facilitates the exploration of new techniques for enhancing the spatial resolution of Sentinel-2 imagery.},
	issn = {2052-4463}
	}
	```

	### Publication 02
	- DOI: [10.1109/LGRS.2024.3401394](https://doi.org/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}
	}
	```

	## 🤝 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\|
	\| :--- \| :--- \| :--- \| :--- \| :--- \| :--- \|
	\|B04\|red\|Band 4 - Red - 10m\|664.5\|29.0\|0\|
	\|B03\|green\|Band 3 - Green - 10m\|560.0\|34.0\|1\|
	\|B02\|blue\|Band 2 - Blue - 10m\|496.5\|53.0\|2\|
	\|B08\|NIR\|Band 8 - Near infrared - 10m\|840.0\|114.0\|3\|