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32 results for “Sentinel-2 Dataset”
SEN2VENµS, a dataset for the training of Sentinel-2 super-resolution algorithms
<p><strong>1 Description</strong></p> <p><strong>SEN2VENµS</strong> 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.</p> <p><strong>Changelog with respect to version 1.0.0</strong> (https://zenodo.org/records/6514159)</p> <ul> <li>All patches are now stored in indivual geoTiFF files with proper geo-referencing, regrouped in zip files per site and per category,</li> <li>The dataset now includes 20 meter resolution SWIR bands B11 and B12 from Sentinel-2 (L2A from Theia). Note that there is no HR reference for those bands, since the VENµS sensor has no SWIR band.</li> </ul> <p><strong>2 Files organization</strong></p> <p>The dataset is composed of separate sub-datasets embedded in separate zip files, one for each site, as described in table <a href="#org5e17b56">1</a>. Note that there might be slight variations in number of patches and number of pairs with respect to version 1.0.0, due do incorrect count of samples in previous version (an empty tensor was still accounted for).</p> <p>Table 1: Number of patches and pairs for each site, along with VENµS viewing zenith angle</p> <table> <tbody> <tr> <th>Site</th> <th>Number of patches</th> <th>Number of pairs</th> <th>VENµS Zenith Angle</th> </tr> </tbody> <tbody> <tr> <td>FR-LQ1</td> <td>4888</td> <td>18</td> <td>1.795402</td> </tr> <tr> <td>NARYN</td> <td>3813</td> <td>24</td> <td>5.010906</td> </tr> <tr> <td>FGMANAUS</td> <td>129</td> <td>4</td> <td>7.232127</td> </tr> <tr> <td>MAD-AMBO</td> <td>1442</td> <td>18</td> <td>14.788115</td> </tr> <tr> <td>ARM</td> <td>15859</td> <td>39</td> <td>15.160683</td> </tr> <tr> <td>BAMBENW2</td> <td>9018</td> <td>34</td> <td>17.766533</td> </tr> <tr> <td>ES-IC3XG</td> <td>8822</td> <td>34</td> <td>18.807686</td> </tr> <tr> <td>ANJI</td> <td>2312</td> <td>14</td> <td>19.310494</td> </tr> <tr> <td>ATTO</td> <td>2258</td> <td>9</td> <td>22.048651</td> </tr> <tr> <td>ESGISB-3</td> <td>6057</td> <td>19</td> <td>23.683871</td> </tr> <tr> <td>ESGISB-1</td> <td>2891</td> <td>12</td> <td>24.561609</td> </tr> <tr> <td>FR-BIL</td> <td>7105</td> <td>30</td> <td>24.802892</td> </tr> <tr> <td>K34-AMAZ</td> <td>1384</td> <td>20</td> <td>24.982675</td> </tr> <tr> <td>ESGISB-2</td> <td>3067</td> <td>13</td> <td>26.209776</td> </tr> <tr> <td>ALSACE</td> <td>2653</td> <td>16</td> <td>26.877071</td> </tr> <tr> <td>LERIDA-1</td> <td>2281</td> <td>5</td> <td>28.524780</td> </tr> <tr> <td>ESTUAMAR</td> <td>911</td> <td>12</td> <td>28.871947</td> </tr> <tr> <td>SUDOUE-5</td> <td>2176</td> <td>20</td> <td>29.170244</td> </tr> <tr> <td>KUDALIAR</td> <td>7269</td> <td>20</td> <td>29.180855</td> </tr> <tr> <td>SUDOUE-6</td> <td>2435</td> <td>14</td> <td>29.192055</td> </tr> <tr> <td>SUDOUE-4</td> <td>935</td> <td>7</td> <td>29.516127</td> </tr> <tr> <td>SUDOUE-3</td> <td>5363</td> <td>14</td> <td>29.998115</td> </tr> <tr> <td>SO1</td> <td>12018</td> <td>36</td> <td>30.255978</td> </tr> <tr> <td>SUDOUE-2</td> <td>9700</td> <td>27</td> <td>31.295256</td> </tr> <tr> <td>ES-LTERA</td> <td>1701</td> <td>19</td> <td>31.971764</td> </tr> <tr> <td>FR-LAM</td> <td>7299</td> <td>22</td> <td>32.054056</td> </tr> <tr> <td>SO2</td> <td>738</td> <td>22</td> <td>32.218481</td> </tr> <tr> <td>BENGA</td> <td>5857</td> <td>28</td> <td>32.587334</td> </tr> <tr> <td>JAM2018</td> <td>2564</td> <td>18</td> <td>33.718953</td> </tr> </tbody> </table> <p> </p> <p>Each site zip file contains a subfolder with the site name. This subfolder contains secondary zip files for each date, following this naming convention as the pair <code>id</code>: <code>{site_name}_{acquisition_date}_{mgrs_tile}</code>. For each date, 5 zip files are available, as shown in table <a href="#org504e2aa">2</a>.Each zip file contain subfolder <code>{bands}/{resolution}/</code> in which one GeoTiFF file per patch is stored, with the following naming convention: <code>{site_name}_{idx}_{acquisition_date}_{mgr_tile}_{bands}_{resolution}.tif</code>. Pixel values are encoded as 16 bits signed integers and should be converted back to floating point surface reflectance by dividing each and every value by 10 000 upon reading.</p> <p>Table 2: Naming convention for zip files associated to each date.</p> <table> <tbody> <tr> <th>File</th> <th>Content</th> </tr> </tbody> <tbody> <tr> <td><code>{id}_05m_b2b3b4b8.zip</code></td> <td>5m patches (\(256\times256\) pix.) for S2 B2, B3, B4 and B8 (from VENµS)</td> </tr> <tr> <td><code>{id}_10m_b2b3b4b8.zip</code></td> <td>10m patches (\(128\times128\) pix.) for S2 B2, B3, B4 and B8 (from Sentinel-2)</td> </tr> <tr> <td><code>{id}_05m_b5b6b7b8a.zip</code></td> <td>5m patches (\(256\times256\) pix.) for S2 B5, B6, B7 and B8A (from VENµS)</td> </tr> <tr> <td><code>{id}_20m_b5b6b7b8a.zip</code></td> <td>20m patches (\(64\times64\) pix.) for S2 B5, B6, B7 and B8A (from Sentinel-2)</td> </tr> <tr> <td><code>{id}_20m_b11b12.zip</code></td> <td>20m patches (\(64\times64\) pix.) for S2 B11 and B12 (from Sentinel-2)</td> </tr> </tbody> </table> <p> </p> <p>Each file comes with a master <code>index.csv</code> CSV (Comma Separated Values) file, with one row for each pair sampled in the given site. Columns are named after the <code>{bands}_{resolution}</code> pattern, and contains the full path to the corresponding GeoTiFF wihin the corresponding zip file:</p> <p><code>{site}_{acquisition_date}_{mgrs_tile}_{bands}_{resolution}.zip/{bands}/{resolution}/{site}_{idx}_{acquisition_date}_{mgrs_tile}_{bands}_{resolution}.tif</code></p> <p><strong>3 Licencing</strong></p> <p><strong>3.1 Sentinel-2 patches</strong></p> <p><strong>3.1.1 Copyright</strong></p> <p>Value-added data processed by CNES for the Theia data centre www.theia-land.fr using Copernicus products. The processing uses algorithms developed by Theia's Scientific Expertise Centres. Note: Copernicus Sentinel-2 Level 1C data is subject to this license: <a href="https://theia.cnes.fr/atdistrib/documents/TC_Sentinel_Data_31072014.pdf">https://theia.cnes.fr/atdistrib/documents/TC_Sentinel_Data_31072014.pdf</a></p> <p><strong>3.1.2 Licence</strong></p> <p>Files <code>*_b2b3b4b8_10m.tif</code>, <code>*_b5b6b7b8a_20m.tif</code> and <code>*_b11b12_20m.tif</code> are distributed under the the original licence of the Sentinel-2 Theia L2A products, which is the Etalab Open Licence Version 2.0 <sup><a href="#fn.2">2</a></sup>.</p> <p><strong>3.2 VENµS patches</strong></p> <p><strong>3.2.1 Copyright</strong></p> <p>Value-added data processed by CNES for the Theia data centre www.theia-land.fr using VENµS satellite imagery from CNES and Israeli Space Agency. The processing uses algorithms developed by Theia's Scientific Expertise Centres.</p> <p>3.2.2 <strong>Licence</strong></p> <p>Files <code>*_b2b3b4b8_05m.tif</code> and <code>*_b5b6b7b8a_05m.tif</code> are distributed under the original licence of the VENµS products, which is Creative Commons BY-NC 4.0 <sup><a href="#fn.3">3</a></sup>.</p> <p><strong>3.3 Remaining files</strong></p> <p>All remaining files are distributed under the Creative Commons BY 4.0 <sup><a href="#fn.4">4</a></sup> licence.</p> <p><strong>4 Note to users</strong></p> <p>Note that even if the VenµS2 dataset is sorted by sites and by pairs, we strongly encourage users to apply the full set of machine learning best practices when using it : random keeping separate pairs (or even sites) for testing purpose, and randomization of patches accross sites and pairs in the training and validation sets.</p> <p><strong>5 Citing</strong></p> <p>Please cite the following data paper (preprint, submitted to <em>MDPI Data</em>) and zenodo link when publishing work derived from this dataset:</p> <p>Michel, J.; Vinasco-Salinas, J.; Inglada, J.; Hagolle, O. SEN2VENµS, a Dataset for the Training of Sentinel-2 Super-Resolution Algorithms. <em>Data</em> <strong>2022</strong>, <em>7</em>, 96. https://doi.org/10.3390/data7070096</p> <p><a href="https://zenodo.org/deposit/6514159">10.5281/zenodo.14603764</a></p> <p><strong>Footnotes:</strong></p> <p><sup><a href="#fnr.1">1</a></sup></p> <p><a href="https://pytorch.org/">https://pytorch.org/</a></p> <p><sup><a href="#fnr.2">2</a></sup></p> <p><a href="https://theia.cnes.fr/atdistrib/documents/Licence-Theia-CNES-Sentinel-ETALAB-v2.0-en.pdf">https://theia.cnes.fr/atdistrib/documents/Licence-Theia-CNES-Sentinel-ETALAB-v2.0-en.pdf</a></p> <p><sup><a href="#fnr.3">3</a></sup></p> <p><a href="https://creativecommons.org/licenses/by-nc/4.0/">https://creativecommons.org/licenses/by-nc/4.0/</a></p> <p><sup><a href="#fnr.4">4</a></sup></p> <p><a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></p>
The photovoltaic inventory dataset in Japan detected in Sentinel-2 imagery
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WHUS2-CRv a global thin cloud removal dataset for Sentinel-2 images——Train part
<p>The training parts of WHUS2-CRv dataset in which the paired cloud and cloud-free Sentinel-2 images are from different regions of the world. The types of land cover are rich and the acquisition dates of the experimental data cover a long time period (from 2015 to 2020) and all seasons.</p> <p>The validation and testing parts can be found on: <a href="https://doi.org/10.5281/zenodo.8035349">https://doi.org/10.5281/zenodo.8035349</a></p> <p>If you use this dataset for your research, please cite us accordingly:</p> <p>#Reference: </p> <p>[1]J. Li, Z. W, Z. Hu, J. Z, M. Li, L. Mo and M. Molinier, “Thin cloud removal in optical remote sensing images based on generative adversarial networks and physical model of cloud distortion,” ISPRS J. Photogramm. Remote Sens., vol. 166, pp. 373-389, Aug. 2020,<a href="http://doi.org/10.1016/j.isprsjprs.2020.06.021">http://doi.org/10.1016/j.isprsjprs.2020.06.021</a>.</p> <p>[2]J. Li, Z. Wu, Z. Hu, Z. Li, Y. Wang, and M. Molinier, “Deep learning based thin cloud removal fusing vegetation red edge and short wave infrared spectral information for Sentinel-2A imagery,” Remote Sens., vol. 13, no. 1, p. 157, Jan. 2021, <a href="http://doi.org/10.3390/rs13010157">http://doi.org/10.3390/rs13010157</a>.</p> <p>[3]J. Li, Y. Zhang, Q. Sheng, Z. Wu, B. Wang, Z. Hu, G. Shen, M. Schmitt, M. Molinier, “Thin Cloud Removal Fusing Full Spectral and Spatial Features for Sentinel-2 Imagery,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 8759-8775, 2022, <a href="http://10.1109/JSTARS.2022.3211857">http://doi.org/10.1109/JSTARS.2022.3211857</a>.</p>
Training dataset for land type detection on Sentinel-2 images (annotations: build-up, rural, forest, water)
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Thin cloud removal dataset for Sentinel-2 images
<p>This is a thin cloud removal dataset for Sentinel-2A images in CR-GAN-PM[1] article. This dataset contains 20 paired thin cloud and clear Sentinel-2 images.</p> <p>If you use this dataset for your research, please cite us accordingly:</p> <p>#Reference: </p> <p>[1] J. Li, Z. W, Z. Hu, J. Z, M. Li, L. Mo and M. Molinier, “Thin cloud removal in optical remote sensing images based on generative adversarial networks and physical model of cloud distortion,” ISPRS J. Photogramm. Remote Sens., vol. 166, pp. 373–389, Aug. 2020. <a href="http://doi.org/10.1016/j.isprsjprs.2020.06.021">http://doi.org/10.1016/j.isprsjprs.2020.06.021</a>.</p> <p><br> [2] J. Li, Z. Wu, Z. Hu, Z. Li, Y. Wang, and M. Molinier, “Deep learning based thin cloud removal fusing vegetation red edge and short wave infrared spectral information for Sentinel-2A imagery,” Remote Sens., vol. 13, no. 1, p. 157, Jan. 2021. <a href="http://doi.org/10.3390/rs13010157">http://doi.org/10.3390/rs13010157</a>.</p>
Dataset for "Deep Learning with remote sensing data for image segmentation: example of rice crop mapping using Sentinel-2 images"
<p>Dataset for "Deep Learning with remote sensing data for image segmentation: example of rice crop mapping using Sentinel-2 images". </p> <p> </p> <p>image_prediction_pt1 and _pt2 have the same content as image_prediction.zip but split in two parts for faster downloading with Google Colab (to avoid time out)</p> <p> </p> <p>Contact</p> <p>Ricardo Dalagnol</p> <p>ricds@hotmail.com</p>
Sentinel-2 parallax-based cloud detection - sample dataset
<p>This repository archives the Sentinel-2 sample data used in:</p> <p>D. Frantz, E. Haß, A. Uhl, J. Stoffels, and J. Hill (2018): Improvement of the Fmask algorithm for Sentinel-2 images: Separating clouds from bright surfaces based on parallax effects. Remote Sensing of Environment 215, 471-481. <a href="https://doi.org/10.1016/j.rse.2018.04.046">https://doi.org/10.1016/j.rse.2018.04.046</a></p>
SD4EO: Sentinel-2 Northern France Patch Dataset (RGB+NIR)
<p>This dataset has been created as part of the deliverables for ESA’s <a href="https://eo4society.esa.int/projects/sd4eo/">SD4EO project</a>.</p> <p>It consists of orthogonal patches from real satellite images of Sentinel-2, covering the visible and near-infrared bands, taken over large areas in northern France. The following tiles are included:</p> <div> <table> <tbody> <tr> <td> <p><strong>Tile ID</strong></p> </td> <td> <p><strong>Sampling Date</strong></p> </td> <td> <p><strong>Number of patches</strong></p> </td> </tr> </tbody> <tbody> <tr> <td> <p>T31TCJ</p> </td> <td> <p>2021/04/15</p> </td> <td> <p>3306</p> </td> </tr> <tr> <td> <p>T31TCN</p> </td> <td> <p>2021/04/25</p> </td> <td> <p>3120</p> </td> </tr> <tr> <td> <p>T31TDN</p> </td> <td> <p>2021/04/25</p> </td> <td> <p>3481</p> </td> </tr> <tr> <td> <p>T31UDP</p> </td> <td> <p>2021/04/25</p> </td> <td> <p>3540</p> </td> </tr> <tr> <td> <p>T31UEP</p> </td> <td> <p>2021/04/25</p> </td> <td> <p>3599</p> </td> </tr> <tr> <td> <p>T30UWU</p> </td> <td> <p>2023/11/07</p> </td> <td> <p>2928</p> </td> </tr> <tr> <td> <p>T31TCL</p> </td> <td> <p>2024/05/09</p> </td> <td> <p>3422</p> </td> </tr> <tr> <td> <p>T31TDL</p> </td> <td> <p>2024/05/09</p> </td> <td> <p>3306</p> </td> </tr> <tr> <td> <p>T31TDM</p> </td> <td> <p>2021/03/31</p> </td> <td> <p>2891</p> </td> </tr> <tr> <td> <p>T31TEM</p> </td> <td> <p>2021/04/27</p> </td> <td> <p>3480</p> </td> </tr> <tr> <td> <p>T31TEN</p> </td> <td> <p>2021/04/27</p> </td> <td> <p>3038</p> </td> </tr> <tr> <td> <p>T31TFM</p> </td> <td> <p>2021/04/27</p> </td> <td> <p>2576</p> </td> </tr> <tr> <td> <p>T31UDQ</p> </td> <td> <p>2023/10/07</p> </td> <td> <p>3599</p> </td> </tr> <tr> <td> <p>T31UEQ</p> </td> <td> <p>2023/10/07</p> </td> <td> <p>3540</p> </td> </tr> <tr> <td> <p>T30TXT</p> </td> <td> <p>2023/10/10</p> </td> <td> <p>3420</p> </td> </tr> <tr> <td> <p>T30TYS</p> </td> <td> <p>2023/10/10</p> </td> <td> <p>3534</p> </td> </tr> <tr> <td> <p>T30UXU</p> </td> <td> <p>2023/10/10</p> </td> <td> <p>3599</p> </td> </tr> <tr> <td> <p>T30UYU</p> </td> <td> <p>2023/10/10</p> </td> <td> <p>3654</p> </td> </tr> <tr> <td> <p>T31TFN</p> </td> <td> <p>2024/05/11</p> </td> <td> <p>3363</p> </td> </tr> <tr> <td> <p>T31UFP</p> </td> <td> <p>2024/05/11</p> </td> <td> <p>3658</p> </td> </tr> <tr> <td> <p>T31UFQ</p> </td> <td> <p>2024/05/11</p> </td> <td> <p>3660</p> </td> </tr> <tr> <td> <p>T30TVK</p> </td> <td> <p>2024/05/11</p> </td> <td> <p>2970</p> </td> </tr> <tr> <td> <p><strong>TOTAL</strong></p> </td> <td> <p><strong>73,684</strong></p> </td> </tr> </tbody> </table> </div> <p> </p> <p>The tiles were selected to minimize cloud cover, which is why the sampling dates are spread across a wide range. Additionally, care was taken to capture images close to winter and late spring, ensuring that lighting conditions were either at dawn or dusk, which helps reduce saturation effects on highly reflective surfaces such as flat industrial rooftops.</p> <p>Each patch covers an area of approximately 1700x1700 meters (though the exact size may vary slightly with latitude). Patches located at the edges of tiles were excluded to avoid potential cutting or distortion issues. Likewise, patches near large bodies of water, including coastlines, were also discarded.</p> <p>It's important to note that, unlike the synthetic image dataset [<a href="https://zenodo.org/records/13208361">link</a>], in this case, the patches are always disjoint.</p> <p>To facilitate the use of these images for training generative AI models (such as GANs and Diffusion models), the patch size has been standardized to 512x512 pixels, providing an effective resolution of 3.3 meters per pixel. The color channels were scaled according to the accumulated histograms in order to embrace most of the energy (>90%): Thus, the values from 0 to 256 in the red channel correspond to the range of 460.0 to 6700.0 in the irradiance captured by Sentinel-2 for the patches in this dataset. The values in the green channel correspond to a range of 740.0 to 5800.0, and the blue channel values span from 400.0 to 4840.0 as recorded by Sentinel-2 sensors.</p> <p>To simplify their usage, the images are stored in lossless PNG format.</p> <p>Each patch is also accompanied by pixel-level labels based on the color-coded annotations from OpenStreetMap data in the dataset <a href="https://zenodo.org/records/13958096">doi:10.5281/zenodo.13958096</a> Both this dataset and the OpenStreetMap-labeled dataset have been used to train a conditional diffusion model, which generated a synthetic dataset of 46.5 GB that can be accessed at this [<a href="https://zenodo.org/records/13208361">link</a>].</p> <p>The file naming convention is straightforward:</p> <p> Patch_<Tile Name>_<Column Number>_<Row Number>_S2.png</p> <p>Columns and rows are always referenced within the same tile.</p> <p> </p> <p>The images from Sentinel satellites, part of the European Union's Copernicus program, are available under an open access policy. Specifically, they are distributed under the <strong>Creative Commons Attribution-ShareAlike 3.0 IGO (CC BY-SA 3.0 IGO)</strong> license. This means that you are free to share, use, and adapt the images, even for commercial purposes, as long as appropriate credit is given, and any derivative work is shared under the same license: <a href="https://open.esa.int/copernicus-sentinel-satellite-imagery-under-open-licence/">Open Access at ESA</a>. This open access policy allows wide usage for research, education, and even commercial applications, with the goal of supporting environmental monitoring and other societal needs. So, <strong>we have selected the closest avaliable licence in Zenodo, as this dataset is a derivative work.</strong></p> <p>The SD4EO Project is funded by ESA’s FutureEO programme under contract no. 4000142334/23/I-DT and is supervised by ESA Φ-lab.</p>
WHUS2-CRv a global thin cloud removal dataset for Sentinel-2 images——Validation and testing parts
<p>The validation and testing parts of WHUS2-CRv dataset in which the paired cloud and cloud-free Sentinel-2 images are from different regions of the world. The types of land cover are rich and the acquisition dates of the experimental data cover a long time period (from 2015 to 2020) and all seasons.</p> <p>If you use this dataset for your research, please cite us accordingly:</p> <p>#Reference: </p> <p>[1]J. Li, Z. W, Z. Hu, J. Z, M. Li, L. Mo and M. Molinier, “Thin cloud removal in optical remote sensing images based on generative adversarial networks and physical model of cloud distortion,” ISPRS J. Photogramm. Remote Sens., vol. 166, pp. 373-389, Aug. 2020,http://doi.org/10.1016/j.isprsjprs.2020.06.021.</p> <p>[2]J. Li, Z. Wu, Z. Hu, Z. Li, Y. Wang, and M. Molinier, “Deep learning based thin cloud removal fusing vegetation red edge and short wave infrared spectral information for Sentinel-2A imagery,” Remote Sens., vol. 13, no. 1, p. 157, Jan. 2021, http://doi.org/10.3390/rs13010157.</p> <p>[3]J. Li, Y. Zhang, Q. Sheng, Z. Wu, B. Wang, Z. Hu, G. Shen, M. Schmitt, M. Molinier, “Thin Cloud Removal Fusing Full Spectral and Spatial Features for Sentinel-2 Imagery,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 8759-8775, 2022, doi: 10.1109/JSTARS.2022.3211857.</p>
Urban monthly land dynamics Sentinel-2 benchmark dataset
<p>The public data set of the paper "<strong>Time-series land cover change detection using deep learning-based temporal semantic segmentation</strong>" uses monthly synthesized Sentinel-2 for time series semantic change detection. A total of 32894 samples were collected. Each timestamp has a land cover type annotation. Anyone can use this data set to conduct further research. We will add more areas in the future. </p> <p><strong>Data description:</strong></p> <ol> <li>The time series length of the sample is 48, 48 months.</li> <li>Label mapping: 0 is water body, 1 is woodland, 2 is grassland, 3 is bare soil, 4 is impervious surface, 5 is cropland.</li> <li>For any implementation details, you can refer to the paper or github.</li> </ol> <p><strong>Paper citations:</strong></p> <p>He H, Yan J, Liang D, Sun Z, Li J, Wang L. Time-series land cover change detection using deep learning-based temporal semantic segmentation. Remote Sensing of Environment. 2024, 305:114101.</p>
CloudSEN12 - a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2
<p><strong>Description</strong></p> <p>CloudSEN12 is a large dataset for cloud semantic understanding that consists of 9880 regions of interest (ROIs). Each ROI has five 5090x5090 meters image patches (IPs) collected on different dates; we manually choose the images to guarantee that each IP inside an ROI matches one of the following cloud cover groups:</p> <p>- clear (0%)</p> <p>- low-cloudy (1% - 25%) </p> <p>- almost clear (25% - 45%)</p> <p>- mid-cloudy (45% - 65%)</p> <p>- cloudy (65% >)</p> <p>An IP is the core unit in CloudSEN12. Each IP contains data from Sentinel-2 optical levels 1C and 2A, Sentinel-1 Synthetic Aperture Radar (SAR), digital elevation model, surface water occurrence, land cover classes, and cloud mask results from eight cutting-edge cloud detection algorithms. Besides, in order to support standard, weakly, and self-/semi-supervised learning procedures, cloudSEN12 includes three distinct forms of hand-crafted labelling data: high-quality, scribble, and no annotation. Consequently, each ROI is randomly assigned to a different annotation group:</p> <ul> <li> <p>2000 ROIs with pixel-level annotation, where the average annotation time is 150 minutes (high-quality group).</p> </li> <li> <p>2000 ROIs with scribble level annotation, where the annotation time is 15 minutes (scribble group).</p> </li> <li> <p>5880 ROIs with annotation only in the cloud-free (0\%) image (no annotation group).</p> </li> </ul> <p>For high-quality labels, we use the Intelligence foR Image Segmentation\cite{iris2019} (IRIS) active learning technology, a system that combines human photo-interpretation and machine learning. For scribble, ground truth pixels were drawn using IRIS but without ML support. Finally, the no annotation dataset is generated automatically, with manual annotation only in the clear image patch. The dataset is already available here: <strong><a href="https://shorturl.at/cgjtz">https://shorturl.at/cgjtz</a></strong>. Check out our website <strong><a href="https://cloudsen12.github.io/">https://cloudsen12.github.io/</a></strong> for examples of how to download the dataset via STAC.</p>
Dataset related to article "Preservation of Axillary Lymph Nodes Compared with Complete Dissection in T1-2 Breast Cancer Patients Presenting One or Two Metastatic Sentinel Lymph Nodes: The SINODAR-ONE Multicenter Randomized Clinical Trial "
<p>This record contains raw data related to article “Preservation of Axillary Lymph Nodes Compared with Complete Dissection in T1-2 Breast Cancer Patients Presenting One or Two Metastatic Sentinel Lymph Nodes: The SINODAR-ONE Multicenter Randomized Clinical Trial"</p> <p>Abstract</p> <p><strong>Background: </strong> The SINODAR-ONE trial is a prospective noninferiority multicenter randomized study aimed at assessing the role of axillary lymph node dissection (ALND) in patients undergoing either breast-conserving surgery or mastectomy for T1-2 breast cancer (BC) and presenting one or two macrometastatic sentinel lymph nodes (SLNs). The endpoints were to evaluate whether SLN biopsy (SLNB) only was associated with worsening of the prognosis compared with ALND in terms of overall survival (OS) and relapse.</p> <p><strong>Methods: </strong> Patients were randomly assigned (1:1 ratio) to either removal of ≥ 10 axillary level I/II non-SLNs followed by adjuvant therapy (standard arm) or no further axillary treatment (experimental arm).</p> <p><strong>Results: </strong> The trial started in April 2015 and ceased in April 2020, involving 889 patients. Median follow-up was 34.0 months. There were eight deaths (ALND, 4; SNLB only, 4), with 5-year cumulative mortality of 5.8% and 2.1% in the standard and experimental arm, respectively (p = 0.984). There were 26 recurrences (ALND 11; SNLB only, 15), with 5-year cumulative incidence of recurrence of 6.9% and 3.3% in the standard and experimental arm, respectively (p = 0.444). Only one axillary lymph node recurrence was observed in each arm. The 5-year OS rates were 98.9% and 98.8%, in the ALND and SNLB-only arm, respectively (p = 0.936).</p> <p><strong>Conclusions: </strong> The 3-year survival and relapse rates of T1-2 BC patients with one or two macrometastatic SLNs treated with SLNB only, and adjuvant therapy, were not inferior to those of patients treated with ALND. These results do not support the use of routine ALND.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.