--- license: - cc0-1.0 language: - en tags: - sentinel-2 - super-resolution - harmonization - synthetic - cross-sensor - temporal pretty_name: sen2naipv2 viewer: false ---
![Dataset Image](taco.png)

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](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.
drawing The spatial coverage of the datasets `sen2naipv2-histmatch` and `sen2naipv2-unet` is illustrated. The low-resolution (LR) patches measure 130 Γ— 130 pixels, while the high-resolution (HR) patches measure 520 Γ— 520 pixels. Blue stars indicate the spatial locations of the cross-sensor subset.
## πŸ”„ Reproducible Example Open In Colab 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**
drawing
## πŸ›°οΈ Sensor Information The sensor related to the dataset: **sentinel2msi** ## 🎯 Task The task associated with this dataset: **super-resolution** ## πŸ“‚ 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)** ## πŸ’¬ Discussion Insights or clarifications about the dataset: **[https://huggingface.co/datasets/tacofoundation/sen2naipv2/discussions](https://huggingface.co/datasets/tacofoundation/sen2naipv2/discussions)** ## πŸ”€ Split Strategy How the dataset is divided for training, validation, and testing: **stratified** ## πŸ“š 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 GΓ³mez-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/)| ## πŸ§‘β€πŸ”¬ 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|