Update README.md

README.md

@@ -11,11 +11,15 @@ tags:
   - cross-sensor
   - temporal
 pretty_name: sen2naipv2
+viewer: false
 ---
 
-<center>
-<img src='taco.png' alt='drawing' width='20%'/>
-</center>
+<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)
+  
+<p>This dataset follows the TACO specification.</p>
+</div>
 
 
 # sen2naipv2
@@ -25,32 +29,39 @@ pretty_name: sen2naipv2
 
 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,
+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 
+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 
+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`), two new variants are introduced in 
+
+In addition to the synthetic dataset (`sen2naipv2-unet`), three new variants are introduced in 
 SEN2NAIPv2:
-1. **sen2naipv2-histmatch:** Identical to `sen2naipv2-unet` but uses histogram matching instead of 
+
+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:** –  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.
+
+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.
+
+4. **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>
@@ -58,25 +69,31 @@ generated synthetically using the same degradation model as the `sen2naipv2-unet
 <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>
+  of the cross-sensor subset.   
+</sup>
 </center>
 
 
-## 🌮 TACO Snippet
+## 🔄 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
 
-# Load the Cloud-Optimized Dataset
-file = "https://huggingface.co/datasets/tacofoundation/tortilla_demo/resolve/main/sen2naipv2_real.tortilla"
-dataset = tacoreader.load(file)
+print(tacoreader.__version__) # 0.4.5
+
+# Remotely load the Cloud-Optimized Dataset 
+dataset = tacoreader.load("tacofoundation:sen2naipv2-unet")
 
 # Read a sample
-sample_idx = 2000
+sample_idx = 4000
 lr = dataset.read(sample_idx).read(0)
 hr = dataset.read(sample_idx).read(1)
 
@@ -84,15 +101,21 @@ hr = dataset.read(sample_idx).read(1)
 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()
 ```
 
-Or in R:
+<center>
+<img src='https://cdn-uploads.huggingface.co/production/uploads/6402474cfa1acad600659e92/0QDq0EttQxwF6f-VCLrIo.png' alt='drawing' width='70%'/>
+</center>
+
 
-```r
-library(tacoreader)
-file <- "https://huggingface.co/datasets/tacofoundation/tortilla_demo/resolve/main/sen2naipv2_real.tortilla"
-dataset <- tacoreader::load(file)
-```
 ## 🛰️ Sensor Information
 
 
@@ -119,42 +142,41 @@ How the dataset is divided for training, validation, and testing: **stratified**
 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.
+- **DOI**: [10.1038/s41597-024-04214-y](10.1038/s41597-024-04214-y)
+- **Summary**: Version 1 of the SEN2NAIPv2 dataset.
 - **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}
+@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](10.1109/LGRS.2024.3401394)
-- **Summary**: Version 1 of the SEN2NAIPv2 dataset.
+- **Summary**: Set of tools to evaluate super-resolution models in the context of Sentinel-2 imagery.
 - **BibTeX Citation**:
 ```bibtex
-@article{aybar2024sen2naip,
-  title={SEN2NAIP \: A large-scale dataset for Sentinel-2 Image Super-Resolution},
+@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={Scientific Data},
-  volume={9},
-  number={1},
-  pages={782},
-  year={2022},
-  publisher={Nature Publishing Group UK London}
+  journal={IEEE Geoscience and Remote Sensing Letters},
+  year={2024},
+  publisher={IEEE}
 }
 ```
 
-
 ## 🤝 Data Providers
 
-
 Organizations or individuals responsible for the dataset.
 |**Name**|**Role**|**URL**|
 | :--- | :--- | :--- |
@@ -164,7 +186,6 @@ Organizations or individuals responsible for the dataset.
 
 ## 🧑‍🔬 Curators
 
-
 Responsible for structuring the dataset in the TACO format.
 |**Name**|**Organization**|**URL**|
 | :--- | :--- | :--- |
@@ -176,7 +197,7 @@ Responsible for structuring the dataset in the TACO format.
 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|
+|B04|red|Band 4 - Red - 10m|664.5|29.0|0|
 |B03|green|Band 3 - Green - 10m|560.0|34.0|1|
-|B04|red|Band 4 - Red - 10m|664.5|29.0|2|
+|B02|blue|Band 2 - Blue - 10m|496.5|53.0|2|
 |B08|NIR|Band 8 - Near infrared - 10m|840.0|114.0|3|