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81 results for “Sentinel 2”
Subpixel optical correlation co-seismic offsets for the Mw 6.4 and Mw 7.1 Ridgecrest, California earthquakes, from Copernicus Sentinel 2 data
<p>Two strong earthquakes (Mw 6.4 and Mw 7.1) took place near Ridgecrest, California, on July 4 2019 and July 6, respectively.</p> <p><a href="https://earthquake.usgs.gov/earthquakes/eventpage/ci38443183/executive">https://earthquake.usgs.gov/earthquakes/eventpage/ci38443183/executive</a></p> <p><a href="https://earthquake.usgs.gov/earthquakes/eventpage/ci38457511/executive">https://earthquake.usgs.gov/earthquakes/eventpage/ci38457511/executive</a></p> <p>In order to assess surface ruptures and the displacement field from the earthquakes, we used subpixel image correlation with Copernicus Sentinel-2 optical imagery (Band 4). MicMac and CosiCorr software was used to to extract the 2D (East-West and North-South) horizontal co-seismic displacement field.</p> <p>Four high-resolution figures are given per method and component (EW and NS). Road network (white lines - from OpenStreetMap) and Quaternary Faults (black polylines) from USGS (<a href="https://earthquake.usgs.gov/hazards/qfaults/">https://earthquake.usgs.gov/hazards/qfaults/</a>) are used for overlay.</p> <p>Rasters are given per software used (MICMAC_ for MicMac and COSI for CosiCorr), with a pixel resolution of 20m. Final product is corrected with detrending (to remove mostly registration errors) and filtered to remove noise. Stripes resulting from pushbroom scanner and orbit errors were not removed at this product (visible as WNW-ESE and NNE-SSW linear parallel stripes).</p> <p>-North-South displacement: positive values to the North.</p> <p>- East-West displacement: positive values to the East.</p> <p>Raster files are projected in UTM Zone 11North WGS84 ( EPSG:32611)</p> <p> </p> <p>A contribution to <strong>CEOS Working Group Disasters:</strong> Seismic Demonstrator</p> <p><strong>Copyright:</strong> Contains modified Copernicus Sentinel data (2019), OpenStreetMap data (2019), Quaternary Fault and Fold Database of the United States - USGS (2019)</p>
Fig. 2 in Fluorescence in fireflies (Coleoptera: Lampyridae): using sentinel prey to investigate a possible aposematic signal
Fig. 2. Relationship between climatic variables and the total number of attacks recorded on artificial prey (both treatments combined) during each wk-long assay conducted from Oct 2017 through May 2018 (n = 11 wk): (a) photoperiod (d length in h), (b) average temperature per wk.
Fig. 2 in Gray wolves as sentinels for the presence of Echinococcus spp. and other gastrointestinal parasites in France
Fig. 2. Spatial distribution of the 911 fecal samples collected from wolves (gray circle) in southeastern France submitted to copro-DNA analyses for identification of gastrointestinal parasites. The size of the circles is proportional to the number of samples collected per municipality. The departments (corresponding to NUTS3 level) are indicated by black lines.
Supraglacial lakes and channels in West Antarctica and Antarctic Peninsula during January 2017 - Sentinel-2 Group 2
<p>The maximum extent of supraglacial lakes and channels in West Antarctica and the Antarctic Peninsula in January 2017 was produced by a Dual-NDWI (Normalised Difference Water Index) approach with thresholds. >2000 individual scenes were captured by Sentinel-2 (S2) and Landsat-8 (L8) satellite sensors during the entire month of January 2017. To obtain maximum coverage on the cloudy Antarctic Peninsula, the time period is extended to February 10, 2017 over this region.</p> <p>This dataset consists of the maximum extent of supraglacial hydrological activity during January 2017 and detailed 10,478 supraglacial features (10,223 lakes and 255 channels), with cumulative area 119.4 square km in total on the West Antarctic ice sheet and Antarctic Peninsula. In addition to the final product, the supraglacial hydrological features from both sensors (23,389 polygons for S2 and 17,571 polygons for L8) overlapping the final map are included. The supraglacial lake and channel polygons are available as digital GIS, Geographic Information System, shapefiles (.shp) and GeoJSON files as well as Google Earth format (.kmz). The code used to produce the lake and channel dataset for each sensor (S2 and L8) is implemented using Python, and can be accessed on Zenodo (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.4906097&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=JPsWDkSk9wqxEcoxMGWzbNgleTFB1NoIFn7t0WlDg3Q%3D&amp;reserved=0">https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.4906097&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=JPsWDkSk9wqxEcoxMGWzbNgleTFB1NoIFn7t0WlDg3Q%3D&amp;reserved=0</a>) . Landsat-8 and Sentinel-2 imagery are freely available at (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fearthexplorer.usgs.gov%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=RidpbAMFz28isbZM6vNZWPMTdl3bl5OxO3SVWvBu6MQ%3D&amp;reserved=0">https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fearthexplorer.usgs.gov%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=RidpbAMFz28isbZM6vNZWPMTdl3bl5OxO3SVWvBu6MQ%3D&amp;reserved=0</a>) and (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscihub.copernicus.eu%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=lZINlehD3i%2BN%2BPSVZgSJnZa%2FruFq2vGHoEnkQGMmq%2Fg%3D&amp;reserved=0">https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscihub.copernicus.eu%2F&amp;data=04%7C01%7Ccorrd%40live.lancs.ac.uk%7Ce16045ed14e34f2cb4f108d92b70565e%7C9c9bcd11977a4e9ca9a0bc734090164a%7C0%7C0%7C637588586902880130%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C2000&amp;sdata=lZINlehD3i%2BN%2BPSVZgSJnZa%2FruFq2vGHoEnkQGMmq%2Fg%3D&amp;reserved=0</a>), respectively.</p> <p>The products provide a scientific benchmark to monitor the development of these features in a warming climate, and thus enhancing our capability to predict the calving and collapse of any ice shelves in the future. The results provide a baseline for future monitoring of supraglacial hydrology and can be particularly useful to train supervised machine learning algorithms. The lake and channel dataset will be valuable as training data for pixel-based or object-based approaches to map large-scale features automatically using machine learning. This dataset can also provide an a-priori lake distribution for studies incorporating synthetic-aperture radar, SAR and other sensors and platforms.</p> <p>Alongside Sentinel-2 Group 1, this dataset provides the 23,389 polygons from S2 imagery.</p>
Unlabeled Sentinel 2 time series dataset (validation): Self-supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only validation data</strong> are available. To download the full pretraining dataset, see <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <caption><strong>Global unlabeled dataset description</strong></caption> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UVU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table> <p> </p>
Unlabeled Sentinel 2 time series dataset (training, T30TUVU): Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only data from the S2 tile T30UVU</strong> are available. To download the full pretraining dataset, see: <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UWU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table>
Unlabeled Sentinel 2 time series dataset (training, T30TYQ): Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only data from the S2 tile T30TYQ</strong> are available. To download the full pretraining dataset, see: <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <caption><strong>Global unlabeled dataset description</strong></caption> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UWU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table>
Unlabeled Sentinel 2 time series dataset : Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p>This repository list all the available repositories, to load the unlabeled Sentinel 2 (S2) L2A dataset used in the article<a href="https://ieeexplore.ieee.org/document/10414422/"> "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series"</a>. This dataset is composed of patch time series acquired over France. For further details, see section IV.A of the pre-print article, available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <ul> <li>The validation dataset is available here : <a href="https://doi.org/10.5281/zenodo.7890452">10.5281/zenodo.7890452</a></li> <li>The training dataset is composed of 9 zenodo repositories, one for each S2 tiles. Here are the available repositories: <ul> <li>T31UEP<a href="http://https://doi.org/10.5281/zenodo.7899943"> 10.5281/zenodo.7899943</a></li> <li>T31TGJ <a href="https://doi.org/10.5281/zenodo.7899237">10.5281/zenodo.7899237</a></li> <li>T30TYS <a href="https://doi.org/10.5281/zenodo.7924193">10.5281/zenodo.7924193</a></li> <li>T31TFN <a href="https://doi.org/10.5281/zenodo.7896621">10.5281/zenodo.7896621</a></li> <li>T31TDL <a href="http://10.5281/zenodo.7896082">10.5281/zenodo.7896082</a></li> <li>T31TDJ <a href="https://doi.org/10.5281/zenodo.7895498">10.5281/zenodo.7895498</a></li> <li>T30UVU <a href="https://doi.org/10.5281/zenodo.7892410">10.5281/zenodo.7892410</a></li> <li>T30TYQ<a href="https://doi.org/10.5281/zenodo.7890542"> 10.5281/zenodo.7890542</a></li> <li>T30TXT <a href="https://doi.org/10.5281/zenodo.7875977">10.5281/zenodo.7875977</a></li> </ul> </li> </ul> <table> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UWU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table>
Unlabeled Sentinel 2 time series dataset (training, T31TDJ): Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only data from the S2 tile T31TDJ</strong> are available. To download the full pretraining dataset, see: <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <caption><strong>Global unlabeled dataset description</strong></caption> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UWU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table>
Unlabeled Sentinel 2 time series dataset (training, T31TFN): Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only data from the S2 tile T31TFN</strong> are available. To download the full pretraining dataset, see: <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <caption><strong>Global unlabeled dataset description</strong></caption> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UWU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table>
Unlabeled Sentinel 2 time series dataset (training, T31TDL): Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only data from the S2 tile T31TDL</strong> are available. To download the full pretraining dataset, see: <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <caption><strong>Global unlabeled dataset description</strong></caption> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UWU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table>
Unlabeled Sentinel 2 time series dataset (training, T31TGJ): Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only data from the S2 tile T31TGJ</strong> are available. To download the full pretraining dataset, see: <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <caption><strong>Global unlabeled dataset description</strong></caption> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UWU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table>
Unlabeled Sentinel 2 time series dataset (training, T31UEP): Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
<p>This is a part of the unlabeled Sentinel 2 (S2) L2A dataset composed of patch time series acquired over France used to pretrain U-BARN. For further details, see section IV.A of the pre-print article "Self-Supervised Spatio-Temporal Representation Learning Of Satellite Image Time Series" available <a href="https://hal.science/hal-04084839">here</a>. Each patch is constituted of the 10 bands [B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12] and the three masks ['CLM_R1', 'EDG_R1', 'SAT_R1']. The global dataset is composed of two disjoint datasets: training (9 tiles) and validation dataset (4 tiles).</p> <p>In this repo,<strong> only data from the S2 tile T31UEP</strong> are available. To download the full pretraining dataset, see: <a href="https://doi.org/10.5281/zenodo.7891924">10.5281/zenodo.7891924</a></p> <table> <caption><strong>Global unlabeled dataset description</strong></caption> <tbody> <tr> <td>Dataset name</td> <td>S2 tiles</td> <td>ROI size</td> <td>Temporal extent</td> </tr> <tr> <td>Train</td> <td> <p>T30TXT,T30TYQ,T30TYS,T30UVU,</p> <p>T31TDJ,T31TDL,T31TFN,T31TGJ,T31UEP</p> </td> <td>1024*1024</td> <td>2018-2020</td> </tr> <tr> <td>Val</td> <td>T30TYR,T30UWU,T31TEK,T31UER</td> <td>256*256</td> <td>2016-2019</td> </tr> </tbody> </table>
Processed Sentinel 1, Sentinel 2 and Copernicus Emergency Management Service data for fine tuning and predicting flood extent with IBM's granite-geospatial-uki-flood-detection model
<p>This dataset contains processed Sentinel 1 Sentinel 2 imagery together with flood event labels extracted from the Copernicus Emergency Management Service. It has been assembled to demonstrate fine tuning and inference of flood event segmentation using granite geospatial foundation models developed by IBM Research. Please see <a href="https://huggingface.co/ibm-granite/granite-geospatial-uki-flooddetection">https://huggingface.co/ibm-granite/granite-geospatial-uki-flooddetection</a> for more information on models and use.</p> <p>Sentinel-1</p> <p>The European Space Agency. 2014. Sentinel-1 Mission. <a href="https://sentinel.esa.int/web/sentinel/copernicus/sentinel-1">https://sentinel.esa.int/web/sentinel/missions/sentinel1</a>. Accessed: 2024-11-25.</p> <p>Sentinel-2</p> <p>The European Space Agency. 2015. Sentinel-2 Mission. <a href="https://sentinel.esa.int/web/sentinel/copernicus/sentinel-2">https://sentinel.esa.int/web/sentinel/missions/sentinel2</a>. Accessed: 2024-11-25.</p> <p>Copernicus Emergency Management Service</p> <p><a href="https://emergency.copernicus.eu/mapping/list-of-activations-rapid">https://emergency.copernicus.eu/mapping/list-of-activations-rapid</a>. Accessed: 2024-11-25. </p> <p><strong>Attribution</strong></p> <p>Contains modified Copernicus Sentinel data [2019-2024]</p> <p>Contains modified Copernicus Service information [2019-2023]</p>
Supplementary Materials: Use of Sentinel 2 imagery to estimate vegetation height in fragments of Atlantic Forest
<p>Supplementary materials for the paper:</p> <p>Use of Sentinel 2 imagery to estimate vegetation height in fragments of Atlantic Forest<br> Paper DOI: <a href="https://doi.org/10.1016/j.ecoinf.2022.101680">https://doi.org/10.1016/j.ecoinf.2022.101680</a></p> <p>This release is the one used for the definitive version of the article.</p>
Fig. 2 in Western striped squirrel Tamiops mcclellandii: A non-avian sentinel species of bird waves
Fig. 2. Close-association between Dicrurus paradiseus and Tamiops mcclellandii.
Labels for land cover classification for Sentinel 2 images in livestock farms
<p>Land cover data labeling for 2022 Sentinel 2 images in livestock farms, mostly in different agroclimatic regions of Spain. The data has a resolution of 10m and the label values correspond to:</p> <ul> <li>0: Unproductive.</li> <li>1: Woodland.</li> <li>2: Pasture.</li> </ul> <p>Dataset produced within the <a href="https://ai4copernicus-project.eu/">AI4Copernicus</a> European H2020 project.</p>
FIGURE 2 in A new species of the sentinel crab Macrophthalmus (Mareotis) Barnes, 1967 (Crustacea: Brachyura: Macrophthalmidae) from Western Australia
FIGURE 2. Holotype Macrophthalmus (Mareotis) pistrosinus sp. nov. (WAM 655-65). Scale intervals 1mm.
FIGURE 2 in A new dwarf sentinel crab from Mayotte Island, western Indian Ocean (Decapoda: Brachyura: Macrophthalmidae)
FIGURE 2. Macrophthalmus (Chaenostoma) lisae sp. nov., a–l, holotype male 3.75 × 4.9 mm (MNHN B32254); a, carapace dorsal face, b, detail of anterolateral margin; c, front view; d, left third maxilliped; e, left cheliped dorso-mesial view; f, left chela, outer view; g, same, inner view; h, left second pereopod, lateral view; i, left third pereopod, lateral view; j, abdomen and telson, outer view; k, right first pleopod, ventral view; l, same, dorso-mesial view, m–o, paratype female ov. 3.85 × 5.1 mm (MNHN B32362); m, left chela, outer view; n, abdomen and telson, outer view; o, left genital vulva, with indication of suture between fifth and sixth thoracic sternites.
FIGURE 2 in The Case of a Rudderfish Highlights the Role of Natural History Museums as Sentinels of Bio-invasions
FIGURE 2. Kyphosus sectatrix (Linnaeus, 1758), specimen captured in 1883 (Doderlein, 1883), stuffed and preserved in the Museum of Zoology of the University of Palermo (voucher code MZPA P-247).
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.