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--- |
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license: |
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- cc0-1.0 |
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language: |
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- en |
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tags: |
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- sentinel-2 |
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- super-resolution |
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- harmonization |
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- synthetic |
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- cross-sensor |
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- temporal |
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pretty_name: sen2naipv2 |
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viewer: false |
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--- |
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<div style="text-align: center; border: 1px solid #ddd; border-radius: 10px; padding: 15px; max-width: 250px; margin: auto; background-color: #f9f9f9;"> |
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<b><p>This dataset follows the TACO specification.</p></b> |
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</div> |
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# sen2naipv2 |
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****A large-scale dataset for Sentinel-2 Image Super-Resolution**** |
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The SEN2NAIPv2 dataset is an extension of [SEN2NAIP](https://huggingface.co/datasets/isp-uv-es/SEN2NAIP), |
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containing 62,242 LR and HR image pairs, about 76% more images than the first version. The dataset files |
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are named **`sen2naipv2-unet-000{1..3}.part.taco`**. This dataset comprises synthetic RGBN NAIP bands at 2.5 and 10 meters, |
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degraded to corresponding Sentinel-2 images and a potential x4 factor. The degradation model to generate |
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the LR pair comprises three sequential steps: (1) Gaussian blurring and bilinear downsampling, (2) reflectance |
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harmonization, and (3) adding noise. Reflectance harmonization is the most critical of these steps. In version |
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1, the harmonization model used a U-Net architecture to convert Gaussian-blurred NAIP images into |
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reflectance-correct Sentinel-2-like imagery. This initial U-Net model was trained on just 2,851 same-day |
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Sentinel-2 and NAIP imagery. In version 2, the U-Net model was retrained. The temporal threshold was expanded |
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from one day to a 2-day range, and the search included the full Sentinel-2 archive available for the |
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USA, increasing the cross-sensor dataset size to 34,640 images. The kernel degradation and noise model |
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components remain consistent between the two versions. |
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In addition to the synthetic dataset (`sen2naipv2-unet`), three new variants are introduced in |
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SEN2NAIPv2: |
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1. **`sen2naipv2-histmatch:`** (61282 samples) - Identical to `sen2naipv2-unet` but uses histogram matching instead of |
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style transfer for reflectance harmonization using the closest Sentinel-2 image. We report the |
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time difference between the NAIP and Sentinel-2 images used for harmonization. |
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2. **`sen2naipv2-crosssensor:`** (8000 samples) – This variant of the SEN2NAIPv2 dataset is smaller than |
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its synthetic counterparts and includes only **real Sentinel-2**. The dataset is restricted to those captured within |
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**a one-day interval** between Sentinel-2 and NAIP sensors. To ensure cloud-free Sentinel-2 images, any with |
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cloud cover exceeding 0 \%, as determined by the [UnetMob-V2 cloud detector](https://cloudsen12.github.io/), |
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were excluded. The dataset reports the 2nd percentile of Pearson correlations calculated within 16x16 kernels |
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(see `correlation` field) between Sentinel-2 images and a Sentinel-2-like version derived from |
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degraded NAIP imagery. This degradation followed a process similar to the **`sen2naipv2-histmatch`**. This |
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metric provides insight into the quality of the match between Sentinel-2 and the low-frequency components |
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of NAIP. Additionally, a strict constraint was applied to the high-resolution images, using real Sentinel-2 data as |
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a reference to further enhance harmonization. |
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3. **`sen2naipv2-temporal`:** A temporal variant of the SEN2NAIPv2 dataset, where the LR are real Sentinel-2 |
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ages and the HR image has been normalized with the closest Sentinel-2 images using only histogram matching. |
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The temporal LR sequences **always** consist of 16 images, with the nearest image captured **always** within |
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0–10 days. |
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<center> |
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<img src='map.png' alt='drawing' width='75%'/> |
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<sup> |
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The spatial coverage of the datasets `sen2naipv2-histmatch` and `sen2naipv2-unet` is illustrated. The low-resolution (LR) patches |
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measure 130 × 130 pixels, while the high-resolution (HR) patches measure 520 × 520 pixels. Blue stars indicate the spatial locations |
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of the cross-sensor subset. |
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</sup> |
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</center> |
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## 🔄 Reproducible Example |
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<a target="_blank" href="https://colab.research.google.com/drive/1HpirWWZvcZlS2LU9uGc1yIzG04Cu1L33"> |
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<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> |
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</a> |
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Load this dataset using the `tacoreader` library. |
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```python |
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import tacoreader |
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import rasterio as rio |
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print(tacoreader.__version__) # 0.4.5 |
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# Remotely load the Cloud-Optimized Dataset |
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dataset = tacoreader.load("tacofoundation:sen2naipv2-unet") |
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#dataset = tacoreader.load("tacofoundation:sen2naipv2-crosssensor") |
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#dataset = tacoreader.load("tacofoundation:sen2naipv2-histmatch") |
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#dataset = tacoreader.load("tacofoundation:sen2naipv2-temporal") |
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# Read a sample |
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sample_idx = 4000 |
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lr = dataset.read(sample_idx).read(0) |
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hr = dataset.read(sample_idx).read(1) |
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# Retrieve the data |
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with rio.open(lr) as src, rio.open(hr) as dst: |
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lr_data = src.read(window=rio.windows.Window(0, 0, 256//4, 256//4)) |
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hr_data = dst.read(window=rio.windows.Window(0, 0, 256, 256)) |
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# Display |
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fig, ax = plt.subplots(1, 2, figsize=(10, 5)) |
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ax[0].imshow(lr_data.transpose(1, 2, 0) / 3000) |
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ax[0].set_title("Low Resolution - Sentinel 2") |
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ax[1].imshow(hr_data.transpose(1, 2, 0) / 3000) |
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ax[1].set_title("High Resolution - NAIP") |
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plt.show() |
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``` |
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**MORE EXAMPLES IN THE PREVIOUS COLAB** |
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<center> |
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<img src='https://cdn-uploads.huggingface.co/production/uploads/6402474cfa1acad600659e92/0QDq0EttQxwF6f-VCLrIo.png' alt='drawing' width='70%'/> |
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</center> |
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## 🛰️ Sensor Information |
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The sensor related to the dataset: **sentinel2msi** |
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## 🎯 Task |
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The task associated with this dataset: **super-resolution** |
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## 📂 Original Data Repository |
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Source location of the raw data:**[https://huggingface.co/datasets/isp-uv-es/SEN2NAIP](https://huggingface.co/datasets/isp-uv-es/SEN2NAIP)** |
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## 💬 Discussion |
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Insights or clarifications about the dataset: **[https://huggingface.co/datasets/tacofoundation/sen2naipv2/discussions](https://huggingface.co/datasets/tacofoundation/sen2naipv2/discussions)** |
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## 🔀 Split Strategy |
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How the dataset is divided for training, validation, and testing: **stratified** |
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## 📚 Scientific Publications |
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Publications that reference or describe the dataset. |
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### Publication 01 |
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- **DOI**: [10.1038/s41597-024-04214-y](https://doi.org/10.1038/s41597-024-04214-y) |
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- **Summary**: Version 1 of the SEN2NAIPv2 dataset. |
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- **BibTeX Citation**: |
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```bibtex |
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@article{aybar2025sen2naipv2, |
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author = {Aybar, Cesar and Montero, David and Contreras, Julio and Donike, Simon and Kalaitzis, Freddie and Gómez-Chova, Luis}, |
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title = {SEN2NAIP: A large-scale dataset for Sentinel-2 Image Super-Resolution}, |
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journal = {Scientific Data}, |
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year = {2024}, |
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volume = {11}, |
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number = {1}, |
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pages = {1389}, |
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doi = {10.1038/s41597-024-04214-y}, |
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url = {https://doi.org/10.1038/s41597-024-04214-y}, |
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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.}, |
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issn = {2052-4463} |
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} |
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``` |
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### Publication 02 |
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- **DOI**: [10.1109/LGRS.2024.3401394](https://doi.org/10.1109/LGRS.2024.3401394) |
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- **Summary**: Set of tools to evaluate super-resolution models in the context of Sentinel-2 imagery. |
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- **BibTeX Citation**: |
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```bibtex |
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@article{aybar2024comprehensive, |
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title={A Comprehensive Benchmark for Optical Remote Sensing Image Super-Resolution}, |
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author={Aybar, Cesar and Montero, David and Donike, Simon and Kalaitzis, Freddie and G{'o}mez-Chova, Luis}, |
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journal={IEEE Geoscience and Remote Sensing Letters}, |
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year={2024}, |
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publisher={IEEE} |
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} |
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``` |
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## 🤝 Data Providers |
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Organizations or individuals responsible for the dataset. |
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|**Name**|**Role**|**URL**| |
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|Image & Signal Processing|host|[https://isp.uv.es/](https://isp.uv.es/)| |
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|USDA Farm Production and Conservation - Business Center, Geospatial Enterprise Operations|producer|[https://www.fpacbc.usda.gov/](https://www.fpacbc.usda.gov/)| |
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|European Space Agency|producer|[https://www.esa.int/](https://www.esa.int/)| |
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## 🧑🔬 Curators |
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Responsible for structuring the dataset in the TACO format. |
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|**Name**|**Organization**|**URL**| |
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| :--- | :--- | :--- | |
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|Cesar Aybar|Image & Signal Processing|[https://csaybar.github.io/](https://csaybar.github.io/)| |
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## 🌈 Optical Bands |
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Spectral bands related to the sensor. |
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|**Name**|**Common Name**|**Description**|**Center Wavelength**|**Full Width Half Max**|**Index**| |
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| :--- | :--- | :--- | :--- | :--- | :--- | |
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|B04|red|Band 4 - Red - 10m|664.5|29.0|0| |
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|B03|green|Band 3 - Green - 10m|560.0|34.0|1| |
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|B02|blue|Band 2 - Blue - 10m|496.5|53.0|2| |
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|B08|NIR|Band 8 - Near infrared - 10m|840.0|114.0|3| |
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