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OPERA Dynamic Surface Water Extent from Harmonized Landsat Sentinel-2 product (Version 1)

This dataset contains Level-3 Dynamic OPERA surface water extent product version 1. The data are validated surface water extent observations beginning April 2023. Known issues and caveats on usage are described under Documentation. The input dataset for generating each product is the Harmonized Landsat-8 and Sentinel-2A/B/C (HLS) product version 2.0. HLS products provide surface reflectance (SR) data from the Operational Land Imager (OLI) aboard the Landsat 8 satellite and the MultiSpectral Instrument (MSI) aboard the Sentinel-2A/B/C satellite. The surface water extent products are distributed over projected map coordinates using the Universal Transverse Mercator (UTM) projection. Each UTM tile covers an area of 109.8 km × 109.8 km. This area is divided into 3,660 rows and 3,660 columns at 30-m pixel spacing. Each product is distributed as a set of 10 GeoTIFF (Geographic Tagged Image File Format) files including water classification, associated confidence, land cover classification, terrain shadow layer, cloud/cloud-shadow classification, Digital elevation model (DEM), and Diagnostic layer. The digital elevation model (DEM) provided as a layer of the DSWx-HLS product (band 10) was generated using the Copernicus DEM 30-m and Copernicus DEM 90-m models provided by the European Space Agency. The Copernicus DEM 30-m and Copernicus DEM 90-m were produced using Copernicus WorldDEM-30 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved. The organizations in charge of the OPERA project, the Copernicus programme, and Airbus Defence and Space GmbH by law or by delegation do not assume any legal responsibility or liability, whether express or implied, arising from the use of this DEM. The OPERA DSWx-HLS product contains modified Copernicus Sentinel data (2023-2025). To access the calibration/validation database for OPERA Dynamic Surface Water Extent Products, please contact podaac@podaac.jpl.nasa.gov

restrictednotspecifiedJun 2025View details →

HLS Sentinel-2 Multi-spectral Instrument Surface Reflectance Daily Global 30m v1.5

The HLSS30 V1.5 data product was decommissioned on January 4, 2022. Users are encouraged to use the improved [HLSS30 V2](https://doi.org/10.5067/HLS/HLSS30.002) data product.The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance data from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) aboard the European Union’s Copernicus Sentinel-2A and Sentinel-2B satellites. The combined measurement enables global observations of the land every 2-3 days at 30 meter (m) spatial resolution. The HLS project uses a set of algorithms to obtain seamless products from OLI and MSI that include atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, illumination and view angle normalization, and spectral bandpass adjustment. The HLSS30 product provides 30 m Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) and is derived from Sentinel-2A and Sentinel-2B MSI data products. The HLSS30 and [HLSL30](https://doi.org/10.5067/HLS/HLSL30.015) products are gridded to the same resolution and Military Grid Reference System ([MGRS](https://hls.gsfc.nasa.gov/products-description/tiling-system/)) tiling system and thus are “stackable” for time series analysis.The HLSS30 product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate COG. There are 13 bands included in the HLSS30 product along with four angle bands and a quality assessment (QA) band. For a more detailed description of the individual bands provided in the HLSS30 product, please see the User Guide.Provisional HLS V1.5 data have not been validated for their science quality and should not be used in science research or applications.Known Issues* Interruptions in data service occurred during a restaging of backlogged data between June 1 and June 15, 2021 for both HLSS30 and HLSL30 version 1.5 data products. During this time period increased errors in the processing workflow resulted in a significant number of data ingestion failures and thus, significant gaps in data availability. Given the pending release of the version 2.0, science quality HLS products, these missing data will not be filled for version 1.5. Users of the provisional version 1.5 products should be aware of the significant data gap in this two week window. The version 2.0 products will incorporate these data back into the archive. If you have any feedback or questions on the data please contact [Customer Services](https://www.earthdata.nasa.gov/centers/lp-daac/contact) or join our HLS conversion on the [Earthdata Forum](https://forum.earthdata.nasa.gov/viewtopic.php?f=7&t=618&hilit=hls&sid=95750d868b6448e0f4360a1473def234).

OPERA Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 product (Version 1)

The Observational Products for End-Users from Remote Sensing Analysis ([OPERA](https://www.jpl.nasa.gov/go/opera)) Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 (HLS) product Version 1 maps vegetation disturbance alerts that are derived from data collected by Landsat 8 and Landsat 9 Operational Land Imager (OLI) and Sentinel-2A, Sentinel-2B, and Sentinel-2C Multi-Spectral Instrument (MSI). A vegetation disturbance alert is detected at 30 meter (m) spatial resolution when there is an indicated decrease in vegetation cover within an HLS pixel. The Level-3 data product also provides additional information about more general disturbance trends and auxiliary generic disturbance information as determined from the variations of the reflectance through the HLS scenes. [HLS](https://lpdaac.usgs.gov/product_search/?collections=HLS&status=Operational&view=list) data represent the highest temporal frequency data available at medium spatial resolution. The combined observations will provide greater sensitivity to land changes, whether of large magnitude/short duration or small magnitude/long duration.The OPERA_L3_DIST-ALERT-HLS (or DIST-ALERT) data product is provided in Cloud Optimized GeoTIFF (COG) format, and each layer is distributed as a separate file. There are 19 layers contained within the DIST-ALERT product. The layers for both vegetation and generic disturbance include disturbance status, loss or anomaly, maximum loss anomaly, disturbance confidence layer, date of disturbance, count of observations with loss anomalies, days of ongoing anomalies, and day of last disturbance detection. Additional layers are vegetation cover percent, historical percent vegetation cover, and data mask. See the Product Specification Document (PSD) for a more detailed description of the individual layers provided in the DIST-ALERT product.The OPERA_L3_DIST-ALERT-HLS product contains modified Copernicus Sentinel data (2020-2025).Known Issues* Additional usage constraints are provided under Section 5 of the Algorithm Theoretical Basis Document (ATBD).

HLS Sentinel-2 Multi-spectral Instrument Vegetation Indices Daily Global 30 m V2.0

The Harmonized Landsat and Sentinel-2 (HLS) project provides consistent data products from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 and Landsat 9 satellites and the Multi-Spectral Instrument (MSI) aboard Europe’s Copernicus Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites. The combined measurement enables global observations of the land every 2–3 days at 30 meter (m) spatial resolution. The HLSS30 Vegetation Indices (HLSS30_VI) product is derived from Sentinel-2A, Sentinel-2B, and Sentinel-2C MSI data products. Vegetation indices combine specific bands of satellite data to quantify various aspects of vegetation. Analysis of vegetation indices allows for tracking changes in vegetation over time, identifying areas of stress or deforestation, and assessing crop health. Vegetation indices provide a reliable and efficient means of understanding the complex dynamics of vegetation health. The HLSS30_VI and HLSL30_VI products are gridded to the same resolution and Military Grid Reference System ([MGRS](https://hls.gsfc.nasa.gov/products-description/tiling-system/)) tiling system and thus are “stackable” for time series analysis.The HLSS30_VI product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate file. Nine indicators of vegetation health are included in the HLSS30_VI product: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Modified Soil Adjusted Vegetation Index (MSAVI), Normalized Difference Moisture Index (NDMI), Normalized Difference Water Index (NDWI), Normalized Burn Ratio (NBR), Normalized Burn Ratio 2 (NBR2), and Triangular Vegetation Index (TVI). See the User Guide for a more detailed description of the individual vegetation health variables provided in the HLSS30_VI product.

HLS Sentinel-2 Multi-spectral Instrument Surface Reflectance Daily Global 30m v2.0

The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance data from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) aboard Europe’s Copernicus Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites. The combined measurement enables global observations of the land every 2–3 days at 30-meter (m) spatial resolution. The HLS project uses a set of algorithms to obtain seamless products from OLI and MSI that include atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, illumination and view angle normalization, and spectral bandpass adjustment. The HLSS30 product provides 30-m Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) and is derived from Sentinel-2A, Sentinel-2B, and Sentinel-2C MSI data products. The HLSS30 and [HLSL30](https://doi.org/10.5067/HLS/HLSL30.002) products are gridded to the same resolution and Military Grid Reference System ([MGRS](https://hls.gsfc.nasa.gov/products-description/tiling-system/)) tiling system and thus are “stackable” for time series analysis.The HLSS30 product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate COG. There are 13 bands included in the HLSS30 product along with four angle bands and a quality assessment (QA) band. See the User Guide for a more detailed description of the individual bands provided in the HLSS30 product.Known Issues* Unrealistically high aerosol and low surface reflectance over bright areas: The atmospheric correction over bright targets occasionally retrieves unrealistically high aerosol and thus makes the surface reflectance too low. High aerosol retrievals, both false high aerosol and realistically high aerosol, are masked when quality bits 6 and 7 are both set to 1 (see Table 9 in the [User Guide](https://lpdaac.usgs.gov/documents/1698/HLS_User_Guide_V2.pdf)); the corresponding spectral data should be discarded from analysis.* Issues over high latitudes: For scenes greater than or equal to 80 degrees north, multiple overpasses can be gridded into a single MGRS tile resulting in an L30 granule with data sensed at two different times. In this same area, it is also possible that Landsat overpasses that should be gridded into a single MGRS tile are actually written as separate data files. Finally, for scenes with a latitude greater than or equal to 65 degrees north, ascending Landsat scenes may have a slightly higher error in the BRDF correction because the algorithm is calibrated using descending scenes.* Fmask omission errors: There are known issues regarding the Fmask band of this data product that impacts HLSL30 data prior to April of 2022. The HLS Fmask data band may have omission errors in water detection for cases where water detection using spectral data alone is difficult, and omission and commission errors in cloud shadow detection for areas with great topographic relief. This issue does not impact other bands in the dataset.* Inconsistent snow surface reflectance between Landsat and Sentinel-2: The HLS snow surface reflectance can be highly inconsistent between Landsat and Sentinel-2. When assessed on same-day acquisitions from Landsat and Sentinel-2, Landsat reflectance is generally higher than Sentinel-2 reflectance in the visible bands.* Unrealistically high snow surface reflectance in the visible bands: By design, the Land Surface Reflectance Code (LaSRC) atmospheric correction does not attempt aerosol retrieval over snow; instead, a default aerosol optical thickness (AOT) is used to drive the snow surface reflectance. If the snow detection fails, the full LaSRC is used in both AOT retrieval and surface reflectance derivation over snow, which produces surface reflectance values as high as 1.6 in the visible bands. This is a common problem for spring images at high latitudes.* Unrealistically low surface reflectance surrounding snow/ice: Related to the above, the AOT retrieval over snow/ice is generally too high. When this artificially high AOT is used to derive the surface reflectance of the neighboring non-snow pixels, very low surface reflectance will result. These pixels will appear very dark in the visible bands. If the surface reflectance value of a pixel is below -0.2, a NO_DATA value of -9999 is used. In Figure 1, the pixels in front of the glaciers have surface reflectance values that are too low. * Unrealistically low reflectance surrounding clouds: Like for snow, the HLS atmospheric correction does not attempt aerosol retrieval over clouds and a default AOT is used instead. But if the cloud detection fails, an artificially high AOT will be retrieved over clouds. If the high AOT is used to derive the surface reflectance of the neighboring cloud-free pixels, very low surface reflectance values will result. If the surface reflectance value of a pixel is below -0.2, a NO_DATA value of -9999 is used.

OPERA Land Surface Disturbance Annual from Harmonized Landsat Sentinel-2 product (Version 1)

The Observational Products for End-Users from Remote Sensing Analysis ([OPERA](https://www.jpl.nasa.gov/go/opera)) Land Surface Disturbance Annual from Harmonized Landsat Sentinel-2 (HLS) product Version 1 summarizes the [DIST-ALERT](https://doi.org/10.5067/SNWG/OPERA_L3_DIST-ALERT-HLS_V1.001) data product into an annual vegetation disturbance data product. Vegetation disturbance is mapped when there is an indicated decrease in vegetation cover within an HLS Version 2 pixel. The product also provides auxiliary generic disturbance information as determined from the variations of the reflectance through the DIST-ALERT scenes to provide information about more general disturbance trends. The DIST-ANN product tracks changes at the annual scale, aggregating changes identified in the DIST-ALERT product. Only confirmed disturbances from the associated year are reported together with the date of initial disturbance. As confirmed disturbances are determined using subsequent cloud-free observations to determine if the loss detections persist, the required number of HLS scenes depends on visibility of the target. Due to this dependency, summarizing the DIST-ALERT in the DIST-ANN product will have some latency contingent on the algorithmic calibration and is detailed in the Algorithm Theoretical Basis Document (ATBD).The OPERA_L3_DIST-ANN-HLS (or DIST-ANN) data product is provided in Cloud Optimized GeoTIFF (COG) format, and each layer is distributed as a separate COG. There are 21 layers contained within the DIST-ANN product: vegetation disturbance status, historical vegetation cover indicator, maximum vegetation cover indicator, maximum vegetation anomaly value, vegetation disturbance confidence layer, date of initial vegetation disturbance, number of detected vegetation loss anomalies, vegetation disturbance duration, date of last observation assessed for vegetation disturbance, and several generic disturbance layers. Each product layer is gridded to the same resolution and tiling system as HLS V2: 30 meter (m) and Military Grid Reference System (MGRS). See the Product Specification Document (PSD) for a more detailed description of the individual layers provided in the DIST-ANN product. The OPERA_L3_DIST-ANN-HLS product contains modified Copernicus Sentinel data (2020-2025).Known Issues* Additional usage constraints are provided under Section 5 of the Algorithm Theoretical Basis Document (ATBD).

opencc-zeroDec 2016View details →

dryad24/100

Data from: Synergetic use of Sentinel-1 and Sentinel-2 for assessments of heathland conservation status

Habitat quality assessments often demand wall-to-wall information about the state of vegetation. Remote sensing can provide this information by capturing optical and structural attributes of plant communities. Although active and passive remote sensing approaches are considered as complementary techniques, they have been rarely combined for conservation mapping. Here, we combined spaceborne multispectral Sentinel-2 and Sentinel-1 SAR data for a remote sensing-based habitat quality assessment of dwarf shrub heathland, which was inspired by nature conservation field guidelines. Therefore, three earlier proposed quality layers representing (1) the coverage of the key dwarf shrub species, (2) stand structural diversity and (3) an index reflecting co-occurring vegetation were mapped via linking in situ data and remote sensing imagery. These layers were combined in an RGB-representation depicting varying stand attributes, which afterwards allowed for a rule-based derivation of pixel-wise habitat quality classes. The links between field observations and remote sensing data reached correlations between 0.70 and 0.94 for modeling the single quality layers. The spatial patterns shown in the quality layers and the map of discrete quality classes were in line with the field observations. The remote sensing based mapping of heathland conservation status showed an overall agreement of 76% with field data. Transferring the approach in time (applying a second set of Sentinel 1 and 2 data) caused a decrease in accuracy to 73%. Our findings suggest that Sentinel-1 SAR contains information about vegetation structure that is complimentary to optical data and therefore relevant for nature conservation. While we think that rule-based approaches for quality assessments offer the possibility for gaining acceptance in both communities applied conservation and remote sensing, there is still need for developing more robust and transferable methods.

opencc-by-4.0Dec 2021View details →

SITS-Former: A pre-trained spatio-spectral-temporal representation model for Sentinel-2 time series classifcation

This is the unlabeled dataset we introduced in the presented paper 'SITS-Former: A pre-trained spatio-spectral-temporal representation model for Sentinel-2 time series classifcation'. This dataset can be used to pre-train a specified deep learning model (such as SITS-Former, CNN-Transformer, ConvLSTM. etc) for patch-based Sentinel-2 time series classification.   In this dataset, each sample corresponds to an unlabeled image patch time series, which is stored as a separate numpy file named 'unlabeled_XXX.npz'. You can use 'np.load' to open a saved '.npz' file and get two arrays (querid by "ts" and "doy") from the returned dictionary.  The code will be released at https://github.com/linlei1214/SITS-Former soon.

opencc-by-4.0Apr 2022View details →

Analysis Ready Sentinel-1 and Sentinel-2 Data for an area of Cyprus (2018-2020)

Data extracted from the Agriculture Data Cube

opencc-by-4.0Jun 2022View details →

Temporal characterization of integrated Landsat-7, Landsat-8 and Sentinel-2 observations over China

The resulting maps for the total number and revisit interval of combined Landsat-7, Landsat-8 and Sentinel-2 (clear) observations at annual and monthly scale over China. These results were derived based on Landsat-7, Landsat-8 and Sentinel-2 Surface Reflectance data from December 2019 to November 2020 within Google Earth Engine (GEE).

openOct 2024View details →

SD4EO: Sentinel-2 Northern France Patch Dataset (RGB+NIR)

This dataset has been created as part of the deliverables for ESA’s <a href="https://eo4society.esa.int/projects/sd4eo/">SD4EO project</a>. 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: <div> <table> <tbody> <tr> <td> Tile ID </td> <td> Sampling Date </td> <td> Number of patches </td> </tr> </tbody> <tbody> <tr> <td> T31TCJ </td> <td> 2021/04/15 </td> <td> 3306 </td> </tr> <tr> <td> T31TCN </td> <td> 2021/04/25 </td> <td> 3120 </td> </tr> <tr> <td> T31TDN </td> <td> 2021/04/25 </td> <td> 3481 </td> </tr> <tr> <td> T31UDP </td> <td> 2021/04/25 </td> <td> 3540 </td> </tr> <tr> <td> T31UEP </td> <td> 2021/04/25 </td> <td> 3599 </td> </tr> <tr> <td> T30UWU </td> <td> 2023/11/07 </td> <td> 2928 </td> </tr> <tr> <td> T31TCL </td> <td> 2024/05/09 </td> <td> 3422 </td> </tr> <tr> <td> T31TDL </td> <td> 2024/05/09 </td> <td> 3306 </td> </tr> <tr> <td> T31TDM </td> <td> 2021/03/31 </td> <td> 2891 </td> </tr> <tr> <td> T31TEM </td> <td> 2021/04/27 </td> <td> 3480 </td> </tr> <tr> <td> T31TEN </td> <td> 2021/04/27 </td> <td> 3038 </td> </tr> <tr> <td> T31TFM </td> <td> 2021/04/27 </td> <td> 2576 </td> </tr> <tr> <td> T31UDQ </td> <td> 2023/10/07 </td> <td> 3599 </td> </tr> <tr> <td> T31UEQ </td> <td> 2023/10/07 </td> <td> 3540 </td> </tr> <tr> <td> T30TXT </td> <td> 2023/10/10 </td> <td> 3420 </td> </tr> <tr> <td> T30TYS </td> <td> 2023/10/10 </td> <td> 3534 </td> </tr> <tr> <td> T30UXU </td> <td> 2023/10/10 </td> <td> 3599 </td> </tr> <tr> <td> T30UYU </td> <td> 2023/10/10 </td> <td> 3654 </td> </tr> <tr> <td> T31TFN </td> <td> 2024/05/11 </td> <td> 3363 </td> </tr> <tr> <td> T31UFP </td> <td> 2024/05/11 </td> <td> 3658 </td> </tr> <tr> <td> T31UFQ </td> <td> 2024/05/11 </td> <td> 3660 </td> </tr> <tr> <td> T30TVK </td> <td> 2024/05/11 </td> <td> 2970 </td> </tr> <tr> <td> TOTAL </td> <td> 73,684 </td> </tr> </tbody> </table> </div>   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. 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. 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. 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. To simplify their usage, the images are stored in lossless PNG format. 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>]. The file naming convention is straightforward:                                           Patch_<Tile Name>_<Column Number>_<Row Number>_S2.png Columns and rows are always referenced within the same tile.   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 Creative Commons Attribution-ShareAlike 3.0 IGO (CC BY-SA 3.0 IGO) 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, we have selected the closest avaliable licence in Zenodo, as this dataset is a derivative work. The SD4EO Project is funded by ESA’s FutureEO programme under contract no. 4000142334/23/I-DT and is supervised by ESA Φ-lab.

opencc-by-4.0Jun 2024View details →

SD4EO - Physically Based Rendering images of human settlements (Sentinel-2 B2B3B4B8)

This dataset contains part of the results of the SD4EO project, including images corresponding to the use case of human settlement monitoring. The dataset contains images that have been simulated corresponding to different sensors of the Sentinel 2 satellites. Each image in the dataset includes pixel-level labels for each element present in the image, ensuring perfect accuracy due to the synthetic nature of the images. This eliminates common errors in manual or semi-automatic labeling processes. Multispectral images included Sentinel 2: Images from bands 2, 3, 4 and 8. Image labeling (Open Street Map color code) <div> <table> <tbody> <tr> <td> Element </td> <td> R </td> <td> G </td> <td> B </td> </tr> <tr> <td> Road </td> <td> 255 </td> <td> 255 </td> <td> 255 </td> </tr> <tr> <td> Building </td> <td> 196 </td> <td> 182 </td> <td> 171 </td> </tr> <tr> <td> Highway </td> <td> 232 </td> <td> 146 </td> <td> 162 </td> </tr> <tr> <td> Industrial zone </td> <td> 234 </td> <td> 203 </td> <td> 228 </td> </tr> <tr> <td> Parking </td> <td> 238 </td> <td> 238 </td> <td> 238 </td> </tr> <tr> <td> Railway </td> <td> 112 </td> <td> 112 </td> <td> 112 </td> </tr> <tr> <td> Residential zone </td> <td> 224 </td> <td> 223 </td> <td> 223 </td> </tr> <tr> <td> Vegetation </td> <td> 174 </td> <td> 223 </td> <td> 163 </td> </tr> <tr> <td> Water </td> <td> 170 </td> <td> 211 </td> <td> 223 </td> </tr> </tbody> </table> (Building Masks) Green - Non residential Red - Residential </div> Image name convention The name convention follows the next schema of fields, separated by the character “_” -          ID number -          Meters per pixel resolution -          Type of image (Sentinel 2 bands, labels or buildings masks): B2B3B4, B8, labels, buildings Image formats PNG format is used to display each band in normalized values [0..1]. TIFF format is used to save each band with floating point precision in the original range of the satellite images. <table> <tbody> <tr> <td> Type of data </td> <td> PNG range </td> <td> TIFF range </td> </tr> <tr> <td> B2 </td> <td> 0 - 255 </td> <td> 0 - 2000 </td> </tr> <tr> <td> B3 </td> <td> 0 - 255 </td> <td> 0 - 2500 </td> </tr> <tr> <td> B4 </td> <td> 0 - 255 </td> <td> 0 - 3000 </td> </tr> <tr> <td> B8 </td> <td> 0 - 255 </td> <td> 1180 - 5736 </td> </tr> </tbody> </table> Creation and funding All the images have been generated using a tool developed in Unity. This tool will be soon available to enable the generation of new datasets. This research work has been funded by the European Space Agency (ESA) under the FutureEO program and the SD4EO project (Contract No.: 4000142334/23/I-DT), supervised by the ESA Φ-lab. License and attribution This dataset is licensed under a <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a> (CC BY 4.0). When using the images from this dataset, please attribute them as follows: "Synthetic images created by the research group ARTEC - IRTIC - University of Valencia".

openapgl-v3Jan 2023View details →

Supporting data to "Sediment source and pathway identification using Sentinel-2 imagery and (kayak-based) lagrangian river profiles on the Vjosa river"

This release contains the supporting data files that are part of the manuscript "Sediment source and pathway identification using Sentinel-2 imagery and (kayak-based) lagrangian river profiles on the Vjosa river."