Datasets:

tacofoundation
/

sen2venus

@@ -1,268 +0,0 @@
----
-license:
-  - cc-by-4.0
-  - cc-by-nc-4.0
-  - etalab-2.0
-language:
-- en
-tags:
-  - sen2venus
-  - sentinel-2
-  - VENµS
-  - venus
-  - super-resolution
-  - harmonization
-  - synthetic
-  - cross-sensor
-  - temporal
-pretty_name: sen2venus
-viewer: false
----
-<div style="text-align: center; border: 1px solid #ddd; border-radius: 10px; padding: 15px; max-width: 250px; margin: auto; background-color: #f9f9f9;">
-![Dataset Image](images/taco.png)
-<b><p>This dataset follows the TACO specification.</p></b>
-</div>
-# SEN2VENµS: A Dataset for the Training of Sentinel-2 Super-Resolution Algorithms
-## Description
-SEN2VENµS is an open dataset designed for the super-resolution of Sentinel-2 images by leveraging simultaneous acquisitions with the VENµS satellite. The dataset includes 10m and 20m cloud-free surface reflectance patches from Sentinel-2, with reference spatially-registered surface reflectance patches at 5-meter resolution from the VENµS satellite, acquired on the same day. This dataset spans 29 locations, with a total of 132,955 patches of 256x256 pixels at 5 meters resolution. It is ideal for training super-resolution algorithms to enhance the spatial resolution of 8 Sentinel-2 bands down to 5 meters.
-## Creators
-- Julien Michel
-- Juan Vinasco-Salinas
-- Jordi Inglada
-- Olivier Hagolle
-### Changelog
-- **Version 1.0.0**: [Changelog Link](https://zenodo.org/records/6514159)
-- **Version 2.0.0**: [Changelog Link](https://zenodo.org/records/14603764)
-In version 2.0.0:
-- All patches are stored in individual geoTIFF files with proper geo-referencing and grouped in zip files per site and per category.
-- The dataset now includes the 20-meter resolution SWIR bands B11 and B12 from Sentinel-2 (L2A from Theia). Note: there is no high-resolution reference for these bands since the VENµS sensor does not include SWIR bands.
-### Files Organization
-The dataset is divided into separate sub-datasets in individual zip files for each site. Note that the number of patches and pairs may slightly vary compared to version 1.0.0 due to the previous incorrect count.
-#### Table 1: Number of Patches and Pairs per Site, with VENµS Viewing Zenith Angle
-| Site      | Number of Patches | Number of Pairs | VENµS Zenith Angle |
-|-----------|-------------------|-----------------|--------------------|
-| FR-LQ1    | 4888              | 18              | 1.795402           |
-| NARYN     | 3813              | 24              | 5.010906           |
-| FGMANAUS  | 129               | 4               | 7.232127           |
-| MAD-AMBO  | 1442              | 18              | 14.788115          |
-| ARM       | 15859             | 39              | 15.160683          |
-| BAMBENW2  | 9018              | 34              | 17.766533          |
-| ES-IC3XG  | 8822              | 34              | 18.807686          |
-| ANJI      | 2312              | 14              | 19.310494          |
-| ATTO      | 2258              | 9               | 22.048651          |
-| ESGISB-3  | 6057              | 19              | 23.683871          |
-| ESGISB-1  | 2891              | 12              | 24.561609          |
-| FR-BIL    | 7105              | 30              | 24.802892          |
-| K34-AMAZ  | 1384              | 20              | 24.982675          |
-| ESGISB-2  | 3067              | 13              | 26.209776          |
-| ALSACE    | 2653              | 16              | 26.877071          |
-| LERIDA-1  | 2281              | 5               | 28.524780          |
-| ESTUAMAR  | 911               | 12              | 28.871947          |
-| SUDOUE-5  | 2176              | 20              | 29.170244          |
-| KUDALIAR  | 7269              | 20              | 29.180855          |
-| SUDOUE-6  | 2435              | 14              | 29.192055          |
-| SUDOUE-4  | 935               | 7               | 29.516127          |
-| SUDOUE-3  | 5363              | 14              | 29.998115          |
-| SO1       | 12018             | 36              | 30.255978          |
-| SUDOUE-2  | 9700              | 27              | 31.295256          |
-| ES-LTERA  | 1701              | 19              | 31.971764          |
-| FR-LAM    | 7299              | 22              | 32.054056          |
-| SO2       | 738               | 22              | 32.218481          |
-| BENGA     | 5857              | 28              | 32.587334          |
-| JAM2018   | 2564              | 18              | 33.718953          |
-<div style="text-align: center;">
-    <img src="images/map_venus.png" alt="Spectral sensitivity response" width="100%">
-    <p><em>Map of Sentinel-2 coverage on Theia (orange), available VENµS sites (green) and 29 selected sites (red) for the dataset. Source: Michel et al. (2022) </em></p>
-</div>
-## Taco dataset
-The dataset has been reformatted to enhance efficiency and usability within the TACO framework. Each path and timestamp now contain **two images: High Resolution (HR) and Low Resolution (LR)**, allowing for structured pairing of data. Additionally, partial reads are supported, meaning users can access metadata and specific subsets of the dataset without downloading the entire collection.
-To improve data accessibility, a **metadata table** has been introduced, providing structured and easily readable information about the dataset. This allows users to query essential details without requiring full downloads.
-Several **preprocessing steps** have been applied to refine the dataset:
-- **Handling of negative pixel values:** Some Sentinel-2 images contained negative pixel values, which have been masked to **0**.
-- **No-data values:** All no-data values are now set to **65525** for consistency.
-- **Data type conversion:** The original dataset was stored in **int16**, but it has been converted to **uint16** for optimized storage and processing.
-- **Compression for GeoTIFF files:** The dataset now utilizes efficient compression settings to reduce storage size while maintaining data quality:
-  - **Compression algorithm:** `zstd`
-  - **Compression level:** `13`
-  - **Predictor:** `2`
-  - **Multithreading:** `20 threads`
-  - **Interleave mode:** `band`
-### **Files Organization (Updated)**
-The dataset is structured into separate sub-datasets, stored as **individual GeoTIFF files** with proper georeferencing. Each file is grouped into compressed ZIP archives based on site and category.
-**Band structure per sample:**
-- **High-Resolution (HR) images**: 8 bands (VENµS does not include SWIR bands).
-- **Low-Resolution (LR) images**: 10 bands (Sentinel-2 includes SWIR bands B11 and B12).
-These modifications significantly improve dataset usability, allowing for more flexible and efficient access to Sentinel-2 and VENµS image pairs.
-## 🔄 Reproducible Example
-<a target="_blank" href="https://colab.research.google.com/drive/1gQh4_bqfdhYi7pNfNd2oD6Ck_Yhoyw3k?usp=sharing">
-  <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
-import matplotlib.pyplot as plt
-import datetime
-print(tacoreader.__version__)
-dataset = tacoreader.load("tacofoundation:sen2venus")
-# Read a sample row
-row = dataset.read(0)
-row_lr = row.iloc[0]
-row_hr = row.iloc[1]
-title_lr = f"HR_{row_lr['region']}_{row_lr['patch_n_id']}_{datetime.datetime.fromtimestamp(row_lr['stac:time_start']).strftime('%Y-%m-%d')}_{row_lr['s2_tile']}"
-title_hr = f"LR_{row_hr['region']}_{row_hr['patch_n_id']}_{datetime.datetime.fromtimestamp(row_hr['stac:time_start']).strftime('%Y-%m-%d')}_{row_hr['s2_tile']}"
-# Retrieve the data
-lr, hr = row.read(0), row.read(1)
-with rio.open(lr) as src_lr, rio.open(hr) as src_hr:
-    lr_data = src_lr.read([1, 2, 3])
-    hr_data = src_hr.read([1, 2, 3])
-# Display
-fig, ax = plt.subplots(1, 2, figsize=(10, 5.5))
-ax[0].imshow(lr_data.transpose(1, 2, 0) / 3000)
-ax[0].set_title(title_lr)
-ax[0].axis('off')
-ax[1].imshow(hr_data.transpose(1, 2, 0) / 3000)
-ax[1].set_title(title_hr)
-ax[1].axis('off')
-plt.tight_layout()
-plt.show()
-```
-<center>
-    <img src='images/sen2venus_example.png' alt='drawing' width='100%'/>
-</center>
-## 🛰️ Sensor Information
-The sensor related to the dataset: **sentinel2msi** and **venus**
-## 🎯 Task
-The task associated with this dataset: **super-resolution**
-## 📂 Original Data Repository
-Source location of the raw data:**[https://zenodo.org/records/14603764](https://zenodo.org/records/14603764)**
-## 💬 Discussion
-Insights or clarifications about the dataset: **[https://huggingface.co/datasets/tacofoundation/sen2venus/discussions](https://huggingface.co/datasets/tacofoundation/sen2venus/discussions)**
-## 🔀 Split Strategy
-All train.
-## 📚 Scientific Publications
-Publications that reference or describe the dataset.
-### Publication 01
-- **DOI**: [10.3390/data7070096](https://doi.org/10.3390/data7070096)
-- **Summary**: Boosted by the progress in deep learning, Single Image Super-Resolution (SISR) has gained a lot of interest in the remote sensing community, who sees it as an opportunity to compensate for satellites’ ever-limited spatial resolution with respect to end users’ needs. This is especially true for Sentinel-2 because of its unique combination of resolution, revisit time, global coverage and free and open data policy. While there has been a great amount of work on network architectures in recent years, deep-learning-based SISR in remote sensing is still limited by the availability of the large training sets it requires. The lack of publicly available large datasets with the required variability in terms of landscapes and seasons pushes researchers to simulate their own datasets by means of downsampling. This may impair the applicability of the trained model on real-world data at the target input resolution. This paper presents SEN2VENµS, an open-data licensed dataset composed of 10 m and 20 m cloud-free surface reflectance patches from Sentinel-2, with their reference spatially registered surface reflectance patches at 5 m resolution acquired on the same day by the VENµS satellite. This dataset covers 29 locations on earth with a total of 132,955 patches of 256 × 256 pixels at 5 m resolution and can be used for the training and comparison of super-resolution algorithms to bring the spatial resolution of 8 of the Sentinel-2 bands up to 5 m.
-- **BibTeX Citation**:
-```bibtex
-@article{michel2022sen2venmus,
-	title        = {Sen2ven$\mu$s, a dataset for the training of sentinel-2 super-resolution algorithms},
-	author       = {Michel, Julien and Vinasco-Salinas, Juan and Inglada, Jordi and Hagolle, Olivier},
-	year         = 2022,
-	journal      = {Data},
-	publisher    = {MDPI},
-	volume       = 7,
-	number       = 7,
-	pages        = 96
-}
-```
-### Publication 02
-- **DOI**: [10.5281/zenodo.14603764](https://doi.org/10.5281/zenodo.14603764)
-- **Summary**: SEN2VENµS is an open dataset for the super-resolution of Sentinel-2 images by leveraging simultaneous acquisitions with the VENµS satellite. The dataset is composed of 10m and 20m cloud-free surface reflectance patches from Sentinel-2, with their reference spatially-registered surface reflectance patches at 5 meters resolution acquired on the same day by the VENµS satellite. This dataset covers 29 locations with a total of 132 955 patches of 256x256 pixels at 5 meters resolution, and can be used for the training of super-resolution algorithms to bring spatial resolution of 8 of the Sentinel-2 bands down to 5 meters.
-- **BibTeX Citation**:
-```bibtex
-@dataset{julien_michel_2025_14603764,
-	title        = {SEN2VENµS, a dataset for the training of Sentinel-2 super-resolution algorithms},
-	author       = {Julien Michel and Juan Vinasco-Salinas and Jordi Inglada and Olivier Hagolle},
-	year         = 2025,
-	month        = {jan},
-	publisher    = {Zenodo},
-	doi          = {10.5281/zenodo.14603764},
-	url          = {https://doi.org/10.5281/zenodo.14603764},
-	version      = {2.0.0}
-}
-```
-## 🤝 Data Providers
-Organizations or individuals responsible for the dataset.
-|**Name**|**Role**|**URL**|
-| :--- | :--- | :--- |
-|CNES / Theia data centre|producer|[https://theia.cnes.fr/](https://theia.cnes.fr/)|
-|Zenodo|host|[https://zenodo.org/record/14603764/](https://zenodo.org/record/14603764/)|
-## 🧑‍🔬 Curators
-Responsible for structuring the dataset in the TACO format.
-|**Name**|**Organization**|**URL**|
-| :--- | :--- | :--- |
-|Julio Contreras|Image & Signal Processing|[https://juliocontrerash.github.io/](https://juliocontrerash.github.io/)|
-## 🌈 Optical Bands
-### Sentinel-2 MSI
-Spectral bands related to the sensor.
-|**Name**|**Common Name**|**Description**|**Center Wavelength (nm)**|**Full Width Half Max (nm)**|**Index**|
-| :--- | :--- | :--- | :--- | :--- | :--- |
-|B01|aerosols|Band 1 - Aerosols - 60m|443.9 (S2A) / 442.3 (S2B)|20.0|0|
-|B02|blue|Band 2 - Blue - 10m|496.6 (S2A) / 492.1 (S2B)|66.0|1|
-|B03|green|Band 3 - Green - 10m|560.0 (S2A) / 559.0 (S2B)|36.0|2|
-|B04|red|Band 4 - Red - 10m|664.5 (S2A) / 665.0 (S2B)|31.0|3|
-|B05|red edge 1|Band 5 - Red Edge 1 - 20m|703.9 (S2A) / 703.8 (S2B)|16.0|4|
-|B06|red edge 2|Band 6 - Red Edge 2 - 20m|740.2 (S2A) / 739.1 (S2B)|15.0|5|
-|B07|red edge 3|Band 7 - Red Edge 3 - 20m|782.5 (S2A) / 779.7 (S2B)|20.0|6|
-|B08|NIR|Band 8 - Near Infrared - 10m|835.1 (S2A) / 833.0 (S2B)|106.0|7|
-|B8A|red edge 4|Band 8A - Red Edge 4 - 20m|864.8 (S2A) / 864.0 (S2B)|22.0|8|
-|B09|water vapor|Band 9 - Water Vapor - 60m|945.0 (S2A) / 943.2 (S2B)|26.0|9|
-|B11|SWIR 1|Band 11 - Shortwave Infrared 1 - 20m|1613.7 (S2A) / 1610.4 (S2B)|92.0|10|
-|B12|SWIR 2|Band 12 - Shortwave Infrared 2 - 20m|2202.4 (S2A) / 2185.7 (S2B)|185.0|11|
-<div style="text-align: center;">
-    <img src="images/sr_images.png" alt="Spectral sensitivity response" width="100%">
-    <p><em>Spectral sensitivity response of corresponding spectral bands between Sentinel-2 (top) and VENµS (bottom). Source: Michel et al. (2022) </em></p>
-</div>