This dataset follows the TACO specification.

sen2naipv2

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

The SEN2NAIPv2 dataset is an extension of 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, 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.

^{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.}

🔄 Reproducible Example

Load this dataset using the tacoreader library.

import tacoreader
import rasterio as rio

print(tacoreader.__version__) # 0.4.5

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

# 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

🛰️ 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

💬 Discussion

Insights or clarifications about the dataset: 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
• Summary: Version 1 of the SEN2NAIPv2 dataset.
• BibTeX Citation:

@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
• Summary: Set of tools to evaluate super-resolution models in the context of Sentinel-2 imagery.
• BibTeX Citation:

@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/
USDA Farm Production and Conservation - Business Center, Geospatial Enterprise Operations	producer	https://www.fpacbc.usda.gov/
European Space Agency	producer	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/

🌈 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