Update README.md

README.md

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----
-license: cc0-1.0
----
+---
+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|