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32 results for “Sentinel-2 Dataset”
DeepOrchidSeries: A Sentinel-2 Dataset to inform convolutional SDMs with twelve-month Sentinel-2 image time-series, Orchid family
<p><strong>Deep Species Distribution Modelling from Sentinel-2 Image Time-series: a Global Scale Analysis on the Orchid Family</strong> </p> <ul> <li><strong><em>DeepOrchidSeries</em></strong> dataset gathers Sentinel-2 image time-series around geolocated orchid occurrences. Seasonal evolutions of the habitats are captured in the twelve-month RGB/IR time-series with 640x640m spatial resolution. It allows novel Species Distribution Models (SDMs) coupled with convolutional networks to take advantage of both spatial and temporal information.</li> <li>Our <strong>associated article</strong> is describing the modeling choices made to shape this ambitious dataset. It is submitted to <a href="https://www.frontiersin.org/research-topics/18336/plant-biodiversity-science-in-the-era-of-artificial-intelligence">https://www.frontiersin.org/research-topics/18336/plant-biodiversity-science-in-the-era-of-artificial-intelligence</a>. We believe such global data, methods and scripts are valuable to the conservation ecology community and especially deep-SDMs users. To our knowledge, no similar ready-to-use dataset is available. In the article, the dataset's temporal dimension is proven to significantly improve SDMs performances.</li> <li><strong><em>sen2patch</em></strong> is the gitlab project gathering the code to create such dataset. It is available at <a href="https://gitlab.inria.fr/jestopin/sen2patch">https://gitlab.inria.fr/jestopin/sen2patch</a>.</li> <li><strong><em>DeepOrchidSeries.csv</em></strong> contains all occurrences-level information. <ul> <li>We advice to load it with: <pre><code class="language-python">import pandas as pd df = pd.read_csv("path/to/DeepOrchidSeries.csv", sep=';') df.columns ['gbifid', 'canonical_name', 'decimallatitude', 'decimallongitude', 'speciesKey', 'cell_index', 'bot_country', 'bot_code', 'lvl2_code', 'continent_code']</code></pre> <ul> <li>'gbifid' is the occurrences GBIF ID</li> <li>'canonical_name', is the species canonical name</li> <li>'decimallatitude', 'decimallongitude' are the species coordinates in decimal degrees</li> <li>'speciesKey' is the species GBIF unique identifier</li> <li>'cell_index' is a unique cell ID in a 0.0025° lon/lat grid partitioning the Earth (used to stratify train/val/test set by geographic blocks)</li> <li>'bot_country', 'bot_code', 'lvl2_code', 'continent_code' are geographic subdivisions defined in <a href="https://github.com/tdwg/wgsrpd">https://github.com/tdwg/wgsrpd</a> (code and string for WGSRPD level 1, the botanical countries)</li> </ul> </li> </ul> </li> <li> <p>Initial <a href="https://www.gbif.org/">GBIF</a> query DOI is <a href="http://https://doi.org/10.15468/dl.4bijtu">https://doi.org/10.15468/dl.4bijtu</a> (26 August 2019).</p> </li> <li><strong><em>DeepOrchidSeries.tar</em></strong> file contains the satellite image time-series and is available at <a href="https://lab.plantnet.org/deeporchidseries/">https://lab.plantnet.org/deeporchidseries/</a> <ul> <li><em>.tar</em> archive measure 286 GB and extends to 432 GB once decompressed.</li> <li>Image time-series relative tree paths are constructed from the occurrences unique GBIF IDs.</li> <li>For a given occurence <em>gbifid</em>, matching patches are located in: <em>final_dataset_by_gbifid/gbifid[-2:]/gbifid[-4:-2]</em>, <em>i.e.</em> in a first folder named with the <em>gbifid</em> last two numbers and a subfolder with the previous two ones. Example: the time-series files matching occurrence 2236837714 are located at <em>final_dataset_by_gbifid/14/77/</em>. </li> <li>Image time-series are composed of twelve 16 bits RGB <em>.png</em> and twelve 16 bits IR <em>.png</em> files containing data identical to the original L1C products, no lossy compression was made. There are one RGB and one IR .png file per month.</li> <li>Patches from month MM/YYYY of occurrence <em>gbifid</em> are named<em> </em><em>RGB_YYYY_MM_gbifid_.png</em> and <em>IR0_YYYY_MM_gbifid_.png</em>.</li> </ul> </li> <li><em><strong>models.zip</strong></em> is the archive containing the four PyTorch models weights described in our article and<strong><em> </em></strong><em><strong>inception_env.py</strong></em> the used Inception V3 architecture. <em><strong>index.json</strong></em> contains the dictionnary linking the models class indexes from 0 to 14128 with our labels <em>speciesKey</em>: {"class_index":speciesKey}.</li> </ul> <p> </p> <ul> <li><strong>ACKNOWLEDGMENTS</strong>: We warmly thank Alexander Zizka et al. for providing us the geographically and taxonomically curated set of Orchids occurrences. This dataset contains modified Copernicus Sentinel data and Copernicus Service information (2018). Sentinel-2 MSI data used were available at no cost from ESA Sentinels Scientific Data Hub.</li> </ul>
Sentinel2GlobalLULC: A dataset of Sentinel-2 georeferenced RGB imagery annotated for global land use/land cover mapping with deep learning (License CC BY 4.0)
<p>Sentinel2GlobalLULC is a deep learning-ready dataset of RGB images from the Sentinel-2 satellites designed for global land use and land cover (LULC) mapping. Sentinel2GlobalLULC v2.1 contains 194,877 images in GeoTiff and JPEG format corresponding to 29 broad LULC classes. Each image has 224 x 224 pixels at 10 m spatial resolution and was produced by assigning the 25th percentile of all available observations in the Sentinel-2 collection between June 2015 and October 2020 in order to remove atmospheric effects (i.e., clouds, aerosols, shadows, snow, etc.). A spatial purity value was assigned to each image based on the consensus across 15 different global LULC products available in Google Earth Engine (GEE). </p> <p> </p> <p>Our dataset is structured into 3 main zip-compressed folders, an Excel file with a dictionary for class names and descriptive statistics per LULC class, and a python script to convert RGB GeoTiff images into JPEG format. The first folder called "Sentinel2LULC_GeoTiff.zip" contains 29 zip-compressed subfolders where each one corresponds to a specific LULC class with hundreds to thousands of GeoTiff Sentinel-2 RGB images. The second folder called "Sentinel2LULC_JPEG.zip" contains 29 zip-compressed subfolders with a JPEG formatted version of the same images provided in the first main folder. The third folder called "Sentinel2LULC_CSV.zip" includes 29 zip-compressed CSV files with as many rows as provided images and with 12 columns containing the following metadata (this same metadata is provided in the image filenames): </p> <ul> <li>Land Cover Class ID: is the identification number of each LULC class</li> <li>Land Cover Class Short Name: is the short name of each LULC class</li> <li>Image ID: is the identification number of each image within its corresponding LULC class </li> <li>Pixel purity Value: is the spatial purity of each pixel for its corresponding LULC class calculated as the spatial consensus across up to 15 land-cover products </li> <li>GHM Value: is the spatial average of the Global Human Modification index (gHM) for each image</li> <li>Latitude: is the latitude of the center point of each image</li> <li>Longitude: is the longitude of the center point of each image</li> <li>Country Code: is the Alpha-2 country code of each image as described in the ISO 3166 international standard. To understand the country codes, we recommend the user to visit the following website where they present the Alpha-2 code for each country as described in the ISO 3166 international standard:https: //www.iban.com/country-codes</li> <li>Administrative Department Level1: is the administrative level 1 name to which each image belongs</li> <li>Administrative Department Level2: is the administrative level 2 name to which each image belongs</li> <li>Locality: is the name of the locality to which each image belongs</li> <li>Number of S2 images : is the number of found instances in the corresponding Sentinel-2 image collection between June 2015 and October 2020, when compositing and exporting its corresponding image tile</li> </ul> <p>For seven LULC classes, we could not export from GEE all images that fulfilled a spatial purity of 100% since there were millions of them. In this case, we exported a stratified random sample of 14,000 images and provided an additional CSV file with the images actually contained in our dataset. That is, for these seven LULC classes, we provide these 2 CSV files:</p> <ul> <li>A CSV file that contains all exported images for this class </li> <li>A CSV file that contains all images available for this class at spatial purity of 100%, both the ones exported and the ones not exported, in case the user wants to export them. These CSV filenames end with "including_non_downloaded_images".</li> </ul> <p>To clearly state the geographical coverage of images available in this dataset, we included in the version v2.1, a compressed folder called "Geographic_Representativeness.zip". This zip-compressed folder contains a csv file for each LULC class that provides the complete list of countries represented in that class. Each csv file has two columns, the first one gives the country code and the second one gives the number of images provided in that country for that LULC class. In addition to these 29 csv files, we provided another csv file that maps each ISO Alpha-2 country code to its original full country name.</p> <p>© <a href="https://doi.org/10.5281/zenodo.5055632">Sentinel2GlobalLULC Dataset </a>by Yassir Benhammou, Domingo Alcaraz-Segura, Emilio Guirado, Rohaifa Khaldi, Boujemâa Achchab, Francisco Herrera & Siham Tabik is marked with Attribution 4.0 International (CC-BY 4.0)</p>
MUDDAT: A SENTINEL-2 IMAGE-BASED MUDDY WATER BENCHMARK DATASET FOR ENVIRONMENTAL MONITORING.
<p>This is a dataset for mapping muddy waters based on Sentinel-2 (L2A products) satellite imagery. The image data are saved as GeoTIFF files and metadata files are provided in json format. There are 19 images in total, based on 16 distinct European Areas of Interest (AOIs), covering a total of 9 countries such as:</p> <ul> <li>Greece</li> <li>Italy</li> <li>France</li> <li>Spain</li> <li>Belgium</li> <li>UK</li> <li>Sweden</li> <li>Finland and</li> <li>Serbia</li> </ul> <p>From the Sentinel-2 L2A products were extracted 10 spectral bands and then resampled to a 10m spatial resolution. All spectral bands used can be found in the Metadata/Source files. The annotated images comprise 3 classes, "Non-muddy", "Muddy" and "Ambiguous". More details about the annotation methodology can be found on the accepted abstract (file: <a href="../api/records/11220437/draft/files/Accepted_Abstract_03_15_2024.pdf/content" target="_blank" rel="noopener noreferrer">Accepted_Abstract_03_15_2024.pdf</a>) or the published paper, that you can find here: <a href="https://doi.org/10.1109/IGARSS53475.2024.10642051" target="_blank" rel="noopener">10.1109/IGARSS53475.2024.10642051</a>.</p>
S1S2-Water: A global dataset for semantic segmentation of water bodies from Sentinel-1 and Sentinel-2 satellite images
<p>The S1S2-Water dataset is a global reference dataset for training, validation and testing of convolutional neural networks for semantic segmentation of surface water bodies in publicly available Sentinel-1 and Sentinel-2 satellite images. The dataset consists of 65 triplets of Sentinel-1 and Sentinel-2 images with quality checked binary water mask. Samples are drawn globally on the basis of the Sentinel-2 tile-grid (100 x 100 km) under consideration of pre-dominant landcover and availability of water bodies. Each sample is complemented with metadata and Digital Elevation Model (DEM) raster from the Copernicus DEM.</p><p>This work was supported by the German Federal Ministry of Education and Research (BMBF) through the project "Künstliche Intelligenz zur Analyse von Erdbeobachtungs- und Internetdaten zur Entscheidungsunterstützung im Katastrophenfall" (AIFER) under Grant 13N15525, and by the Helmholtz Artificial Intelligence Cooperation Unit through the project "AI for Near Real Time Satellite-based Flood Response" (AI4FLOOD) under Grant ZT-IPF-5-39. </p>
A harmonized Landsat Sentinel-2 (HLS) dataset for benchmarking time series reconstruction methods of vegetation indices
<p>Satellite images can be used to derive time series of vegetation indices, such as normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI), at global scale. Unfortunately, recording artifacts, clouds, and other atmospheric contaminants impacts a significant portion of the produced images, requiring the usage of ad-hoc techniques to reconstruct the time series in the affected regions. In literature, several methods have been proposed to fill the gaps present in the images, and some works also presented performance comparisons between them (Roerink et al., 2000; Moreno-Martínez et al., 2020; Siabi et al., 2022). Because of the lack of a ground truth for the reconstructed images, the performance evaluation requires the creation of datasets where artificial gaps are introduced in a reference image, such that metrics like the root mean square error (RMSE) can be computed comparing the reconstructed images with the reference one. Different approaches have been used to create the reference images and the artificial gaps, but in most cases, the artificial gaps are introduced using arbitrary patterns and/or the reference image is produced artificially and not using real satellite images (e.g. Kandasamy et al., 2013; Liu et al., 2017; Julien & Sobrino, 2018). In addition, to the best of our knowledge, few of them are openly available and directly accessible allowing for fully reproducible research.</p> <p>We provide here a benchmark dataset for time series reconstruction method based on the<strong> <a href="https://hls.gsfc.nasa.gov/">harmonized Landsat Sentinel-2 (HLS)</a> </strong>collection where the artificial gaps are introduced with a realistic spatio-temporal distribution. In particular, we selected six tiles that we considered representative for most of the main climate classes (e.g. equatorial, arid, warm temperature, boreal and polar), as depicted in the preview.</p> <p>Specifically, following the <strong><a href="https://hls.gsfc.nasa.gov/products-description/tiling-system/">relative tiling system</a></strong> shown above, we downloaded the Red, NIR and F-mask bands from both the HLSL30 and HLSS30 collections for the tiles 19FCV, 22LEH, 32QPK, 31UFS, 45WFV and 49MWM. From the Red and NIR band we derived the NDVI as:</p> <p><span class="math-tex">\(NDVI = {NIR - Red \over NIR + Red}\)</span></p> <p>only for clear-sky on lend pixels (F-mask bits 1, 3, 4 and 5 equal zero), setting as not a number the remaining pixels. The images are then aggregated on a 16 days base, averaging the available values for each pixel in each temporal range. The so obtained data, are considered from us as the reference data for the benchmarking, and stored following the file naming convention</p> <p><em>HLS.T<TILE_NAME>.<YYYYDDD>.v2.0.NDVI.tif</em></p> <p>where <em>TILE_NAME</em> is one between the above specified ones, <em>YYYY</em> is the corresponding year (spanning from 2015 to 2022) and <em>DDD</em> is the day of the year from which the corresponding 16 days range starts. Finally, for each tile, we have a time series composed of <strong>184</strong> images (23 images for 8 years) that can be easily manipulated, for example using the <strong><a href="https://github.com/scikit-map/scikit-map/tree/master">Scikit-Map library</a></strong> in Python.</p> <p>Starting from those data, for each image we considered the mask of currently present gaps, we randomly rotated it by 90, 180 or 270 degrees and we added artificial gaps in the pixels of the rotated mask. Doing so, we believe that the spatio-temporal distribution will be still realistic, providing a solid benchmark for gap-filling methods that work on time series, on spatial pattern or combination of the both.</p> <p>The data including the artificial gaps are stored with the naming structure</p> <p><em>HLS.T<TILE_NAME>.<YYYYDDD>.v2.0.NDVI_art_gaps.tif</em></p> <p>following the previously mentioned convention. The performance metrics, such as RMSE or normalized RMSE (NRMSE), can be computed by applying a reconstruction method on the images with artificial gaps, and then comparing the reconstructed time series with the reference one only on the artificially created gaps locations. </p> <p>This dataset was used to compare the performance of some gap-filling methods and we provide a <strong><a href="https://github.com/OpenGeoHub/EO-benchmark/blob/main/gap_filling_methods/gap_filling_comparison.ipynb">Jupyter notebook</a></strong> that shows how to access and use the data. The files are provided in GeoTIFF format and projected in the coordinate reference system WGS 84 / UTM zone 19N (EPSG:32619). </p> <p>If you succeed to produce higher accuracy or develop a new algorithm for gap filling, please contact authors or post on our GitHub repository. May the force be with you!</p> <p>References:</p> <ol> <li> <p>Julien, Y., & Sobrino, J. A. (2018). TISSBERT: A benchmark for the validation and comparison of NDVI time series reconstruction methods. Revista de Teledetección, (51), 19-31. <a href="https://doi.org/10.4995/raet.2018.9749">https://doi.org/10.4995/raet.2018.9749</a> </p> </li> <li> <p>Kandasamy, S., Baret, F., Verger, A., Neveux, P., & Weiss, M. (2013). A comparison of methods for smoothing and gap filling time series of remote sensing observations–application to MODIS LAI products. Biogeosciences, 10(6), 4055-4071. <a href="https://doi.org/10.5194/bg-10-4055-2013">https://doi.org/10.5194/bg-10-4055-2013</a> </p> </li> <li> <p>Liu, R., Shang, R., Liu, Y., & Lu, X. (2017). Global evaluation of gap-filling approaches for seasonal NDVI with considering vegetation growth trajectory, protection of key point, noise resistance and curve stability. Remote Sensing of Environment, 189, 164-179. <a href="https://doi.org/10.1016/j.rse.2016.11.023">https://doi.org/10.1016/j.rse.2016.11.023</a> </p> </li> <li> <p>Moreno-Martínez, Á., Izquierdo-Verdiguier, E., Maneta, M. P., Camps-Valls, G., Robinson, N., Muñoz-Marí, J., ... & Running, S. W. (2020). Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud. Remote Sensing of Environment, 247, 111901.<a href="https://doi.org/10.1016/j.rse.2020.111901"> https://doi.org/10.1016/j.rse.2020.111901</a> </p> </li> <li> <p>Roerink, G. J., Menenti, M., & Verhoef, W. (2000). Reconstructing cloudfree NDVI composites using Fourier analysis of time series. International Journal of Remote Sensing, 21(9), 1911-1917. <a href="https://doi.org/10.1080/014311600209814">https://doi.org/10.1080/014311600209814</a></p> </li> <li> <p>Siabi, N., Sanaeinejad, S. H., & Ghahraman, B. (2022). Effective method for filling gaps in time series of environmental remote sensing data: An example on evapotranspiration and land surface temperature images. Computers and Electronics in Agriculture, 193, 106619.<a href="https://doi.org/10.1016/j.compag.2021.106619"> https://doi.org/10.1016/j.compag.2021.106619</a></p> </li> </ol>
Reference dataset for comparison of cloud detection algorithms for Sentinel-2 imagery
<p>Sentinel-2 cloud mask reference dataset generated and analyzed as part of Tarrio, K., Tang, X., Masek, J.G., Claverie, M., Ju, J., Qiu, S., Zhu, Z. and Woodcock, C.E., 2020. Comparison of cloud detection algorithms for Sentinel-2 imagery. Science of Remote Sensing, 2, p.100010. [https://www.sciencedirect.com/science/article/pii/S2666017220300092](https://www.sciencedirect.com/science/article/pii/S2666017220300092)</p> <p><strong>1. Reference masks</strong></p> <p><strong>Algorithms:</strong></p> <ul> <li>Fmask 1.x</li> <li>Fmask 2.x</li> <li>Fmask 4.x</li> <li>Tmask</li> <li>Sen2Cor</li> <li>MAJA</li> <li>LaSRC</li> </ul> <p><strong>Locations:</strong></p> <ul> <li>South Africa (35JPM)</li> <li>Senegal (28PDC)</li> <li>Switzerland (32TLT)</li> <li>France (31TCJ, 31TFJ)</li> <li>Morocco (29RNQ)</li> </ul> <p><strong>Standardized legend:</strong></p> <p>Original algorithm outputs were standardized to the same categorical legend.</p> <ul> <li>0 = clear land</li> <li>1 = clear water</li> <li>2 = cloud shadow</li> <li>3 = snow/ice</li> <li>4 = cloud</li> </ul> <p>All reference masks processed to both 10m and 30m resolution, with the exception of Tmask, which is available only at a 30m resolution.</p> <p><strong>Mask naming convention:</strong></p> <p>All processed masks are named according to the following convention:<br> M<*resolution*><*S2 MGRS tile ID*><*YYYY*><*DOY*><*algorithm*><br> e.g. **M30T28PDC2016351TMASK**</p> <p><br> <strong>2. Interpreted sample points</strong></p> <p>Sample points were selected based on agreement among different map products. This record includes a shapefile with the final interpretations for each of the sampled sites. (See publication for additional information.)</p>
Codes and dataset of the publication "Effectiveness of Sentinel-1 and Sentinel-2 for Flood Detection Assessment in Europe"
<p>The folder contains the codes, input and output of the analysis carried out for supporting the publication of the paper:</p> <p>Tarpanelli A., Mondini A., Camici S.:Effectiveness of Sentinel-1 and Sentinel-2 for Flood Detection Assessment in Europe, Natural Hazards and Earth System Sciences, https://doi.org/10.5194/nhess-2022-63, 2022.</p> <p> </p> <p>The codes should be run in order A1-A7 to generate all the figures of the paper.</p> <p>For details please send an email to:</p> <p>angelica.tarpanelli@irpi.cnr.it</p> <p> </p>
Dataset of processed Sentinel-2 images for chlorophyll-a estimation in high-altitude lakes in the Sierra Nevada, Spain
<p>This dataset contains Sentinel 2 satellite images clipped to 5 high-altitude lakes in the Sierra Nevada Mountain Range, Spain. The images were processed with the following atmospheric correction algorithms:</p><ul><li><a href="https://c2rcc.org/">C2RCC</a> (<a href="https://ui.adsabs.harvard.edu/abs/2016ESASP.740E..54B/abstract">Brockmann et al. 2016</a>)</li><li><a href="https://github.com/MarcYin/SIAC">SIAC</a> (<a href=" https://doi.org/10.5194/gmd-15-7933-2022">Yin et al. 2022)</a></li><li><a href="https://github.com/acolite/acolite/releases/tag/20221114.0">ACOLITE</a> (<a href="https://doi.org/10.1016/j.rse.2018.07.015">Vanhellemont & Ruddick, 2018</a>)</li><li><a href="https://grass.osgeo.org/grass83/manuals/i.atcorr.html">6SV</a> (<a href="https://doi.org/10.1109/36.581987">Vermote et al. 2006</a>)</li></ul><p><strong>Included Lakes and and their IDs:</strong></p><ul><li>Laguna de la Caldera (ID = P-2)</li><li>Laguna-embalse de las Yeguas (ID = D-6)</li><li>Laguna de Río Seco (ID = P-8)</li><li>Laguna Larga (ID = G-7)</li><li>Laguna de la Mosca (ID = G-11)</li></ul>
Evaluation of Spatiotemporal Fusion Methods Using Sentinel-2 And Sentinel-3: A New Benchmark Dataset And Comparison
<p>In Earth observation, data fusion is important to generate high temporal and spatial resolution images. Nevertheless, existing research on data fusion primarily concentrates on merging two sources of data (mostly MODIS and Landsat). Therefore, we offer the community a new benchmark dataset for evaluating data fusion using new European sensors (Sentinel-2 and Sentinel-3).</p> <p>The dataset is composed of three different sites located in different parts of the world to ensure the diversity of the ecosystem. The two components of the dataset are collected from operating missions ( Sentinel-2 and Sentinel-3). We also provide 10 bands for Sentinel-2 ranging from blue to SWIR, 4 bands at 10m resolution and 6 at 20m resolution. For Sentinel-3 16 bands are provided with a spatial resolution of 300m. The multiple bands allow for different applications for this dataset such as testing data fusion methods, etc.</p>
Quantifying wetness variability in aapa mires with Sentinel-2: towards improved monitoring of an EU priority habitat [Dataset]
<p>This repository contains data used in Jussila et al. 2023 paper "Quantifying wetness variability in aapa mires with Sentinel-2: towards improved monitoring of an EU priority habitat" (submitted). The files uploaded in the repository include (i) spatial polygon data of flark mires located in Finnish aapa mire occurrence zone, (ii) polygon subset of the flark mires observed in the study, focused on mires belonging to Natura 2000 network (ii) monthly information for April-September period in 2017-2020 of climatic water balance and Sentinel-2 -derived wetness metrics, as average values per mire, and (iii) training point data used to train the decision tree model which was used in the study to detect wet flark surfaces in the studied aapa mires. Retrieving the metrics from Sentinel-2 satellite imagery for the analysis of variability in wetness was executed with Sentinelhub Batch statistical API, and the process is documented in project GitHub repository: <a href="https://github.com/sykefi/feo-aapa">https://github.com/sykefi/feo-aapa</a>. </p> <p>Additional information of data is provided in the README file.</p>
CBRA: The first multi-annual (2016-2021) and high-resolution (2.5 m) building rooftop area dataset in China derived with Super-resolution Segmentation from Sentinel-2 imagery
<p>Large-scale and up-to-date maps of building rooftop area (BRA) are crucial for addressing policy decisions and sustainable development. In addition, as a fine-grained indicator of human activities, BRA could contribute to urban planning and energy modeling to provide benefits to human well-being. However, existing large-scale BRA datasets, such as those from Microsoft and Google, do not include China, hence there are no full-coverage maps of BRA in China. To this end, we produce the multi-annual China building rooftop area dataset (CBRA) with 2.5 m resolution from 2016-2021 Sentinel-2 images. The CBRA is the first full-coverage and multi-annual BRA data in China. The CBRA achieves good performance with the F1 score of 62.55% (+10.61% compared with the previous BRA data in China) based on 250,000 testing samples in urban areas, and the recall of 78.94% based on 30,000 testing samples in rural areas. </p> <p>The CBRA is organized as GeoTIFF (.tif) raster file format with a single band and GCS_WGS_1984 coordinate system. The pixel values are 0 and 255, with 0 representing the background and 255 representing the building rooftop area. Furthermore, to facilitate the use of the data, the CBRA is split into 215 tiles of spatial grid, named “CBRA_year_E/W**N/S**.tif”, where “year” is the sampling year, the “E/W**N/S**” is the latitude and longitude coordinates found in the upper left corner of the tile data.</p> <p> </p> <p>Version 2.0: In version 1.0, there were empty raster images (because they didn't contain buildings). In version 2.0, these raster images were removed.</p>
Sentinel-2 urban green training dataset
<p>Training dataset for urban green land cover and land use detection for Sentinel-2 satellite images. Samples are pixel-wise labelled scenes over the city of Prague, including bigger parks and smaller vegetation patches within high-density urban areas.</p> <p> </p> <p>Contains four classes:</p> <p>* 0: Non-vegetated pixels<br> * 1: Low recreational vegetation<br> * 2: High recreational vegetation<br> * 3: Non-recreational vegetation</p>
A spatiotemporal dataset for NDVI prediction on Sentinel-2 imagery
<p>The dataset was designed for prediction of vegetation health on Sentinel-2 imagery and utilized in the AI4Copernicus service: "Long Short-Term Memory Neural Network for NDVI prediction" in which an LSTM neural network was trained in order to create an AI model for NDVI (Normalized Difference Vegetation Index) prediction with time-horizon of one month. </p><p>The dataset needs to be enriched with more data in time dimension but currently it can be used for initial experimentation with an LSTM neural network for NDVI prediction.</p>
A dataset for tree-crops prediction in Sentinel-2 imagery
<p>The dataset was designed for semantic-segmentation-based deep learning models and utilized in the AI4Copernicus service: "Deep network for pixel-level classification of S2 patches" in which a U-net neural network was used in order to create an AI model for tree-crops prediction.</p><p>The dataset needs to be enriched with more data. However, it can be used for initial experimentation with a U-net neural network and satellite imagery.</p>
Automatic Detection of Photovoltaic from Sentinel-2 observations by an Enhanced U-Net method - DataSet
<p>Data Availability for <strong>Enhanced U-Net(E-UNET)</strong></p> <p>Thank you for your interest in our dataset.<br> <strong>Repository contents:</strong><br> <em>Sentinel-2 L2A product</em>: The tiles that we selected containing photovoltaic.<br> <em>ImageFusion_Result</em>: The image fusion results of ROI including photovoltaic which we intercepted from the Sentinel-2 data. It contains 10m resolution, 10m and 20m resolution, 10m, 20m and 60m resolution fusion results, and RGB 3-channels fusion results.<br> <em>Image</em>: The RGB images for the ROI containing photovoltaic.<br> <em>Label</em>: The manual annotations for the ROI containing photovoltaic.</p>
Pixel-based forest classification of Sentinel-2 images using automatically generated datasets
<p>Contains six training datasets, composed of 800, 1600 and 3200 images. Each training dataset made up of OSM (<em>OpenStreetMap</em>) masks or HRL (<em>Copernicus pan-European High Resolution Layers</em>).</p> <p>Additional 2 evaluation datasets based on OSM and HRL. Composed of 200 evaluation images.</p> <p>For study area <em>lithuania_2018_06.tiff</em> is provided. This contains a fully preprocessed study area (removed clouds, composed mosaic).</p> <p>We provide additionally a merged mosaic of Lithuanian HRL in <em>lithuania_HRL.tiff </em>file.</p> <p><em>OpenStreetMap</em> database is not provided, it can be found at https://planet.openstreetmap.org.</p>
WHUS2-CR, a thin cloud removal dataset for Sentinel-2 images
<p>WHUS2-CR is a thin cloud removal dataset for Sentinel-2A images. WHUS2-CR contains 36 paired cloud and corresponding clear Sentinel-2A images evenly distributed over the world.</p> <p><strong>Because the max storage limitation of one dataset is 50 GB, 5 files can not be uploaded on this dataset. They can be found on : <a href="https://doi.org/10.5281/zenodo.5616753">https://doi.org/10.5281/zenodo.5616753</a>. (The reported results in ref [1] are in CRMSS-result.rar, CRMSS-10-4.rar and CRMSS-4-4.rar, respectively.)</strong></p> <p><strong>The dataset reproducing code and model source code for ref [2] are on :</strong> <a href="https://github.com/Neooolee/WHUS2-CR">https://github.com/Neooolee/WHUS2-CR</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><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> <p>The training and testing image list used in reference [2] is (The training and testing small patches are listed in <a href="https://zenodo.org/api/files/bae57b05-f2cc-4729-b97c-eec7c215e9e4/filenos.xlsx">filenos.xlsx</a>):</p> <p>Training set</p> <table> <tbody> <tr> <td>1</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20160403T030602_N0201_R075_T50TMK_20160403T031209</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20160413T031632_N0201_R075_T50TMK_20160413T031626</td> </tr> <tr> <td>2</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20181111T053041_N0207_R105_T43RGM_20181111T083104</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20181121T053121_N0207_R105_T43RGM_20181121T091419</td> </tr> <tr> <td>3</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20160925T104022_N0204_R008_T32ULB_20160925T104115</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20160915T104022_N0204_R008_T32ULB_20160915T104018</td> </tr> <tr> <td>4</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20160528T153912_N0202_R011_T18TWL_20160528T154746</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20160518T155142_N0202_R011_T18TWL_20160518T155138</td> </tr> <tr> <td>5</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20181208T170701_N0207_R069_T14QMG_20181208T202913</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20181218T170711_N0207_R069_T14QMG_20181218T203015</td> </tr> <tr> <td>6</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20180809T190911_N0206_R056_T10UFB_20180810T002400</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20180819T190911_N0206_R056_T10UFB_20180820T002955</td> </tr> <tr> <td>7</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190306T132231_N0207_R038_T22KHV_20190306T164115</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190224T132231_N0207_R038_T22KHV_20190224T164104</td> </tr> <tr> <td>8</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20181012T084901_N0206_R107_T37VCC_20181012T110218</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20181022T085011_N0206_R107_T37VCC_20181022T110901</td> </tr> <tr> <td>9</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190619T023251_N0207_R103_T50JKP_20190619T071925</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190629T023251_N0207_R103_T50JKP_20190629T053618</td> </tr> <tr> <td>10</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190901T032541_N0208_R018_T47NPF_20190901T070148</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190911T032541_N0208_R018_T47NPF_20190911T084555</td> </tr> <tr> <td>11</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20160419T083012_N0201_R021_T36RUU_20160419T083954</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20160409T083012_N0201_R021_T36RUU_20160409T084024</td> </tr> <tr> <td>12</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190218T143751_N0207_R096_T19HCC_20190218T175945</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190208T143751_N0207_R096_T19HCC_20190208T180253</td> </tr> <tr> <td>13</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20180609T061631_N0206_R034_T42TWL_20180609T081837</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20180530T061631_N0206_R034_T42TWL_20180530T082050</td> </tr> <tr> <td>14</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20191111T025941_N0208_R032_T49QGF_20191111T055938</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20191101T025841_N0208_R032_T49QGF_20191101T054434</td> </tr> <tr> <td>15</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190818T103031_N0208_R108_T31SEA_20190818T124651</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190808T103031_N0208_R108_T31SEA_20190808T124427</td> </tr> <tr> <td>16</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20191202T105421_N0208_R051_T29PPP_20191202T112025</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20191212T105441_N0208_R051_T29PPP_20191212T111831</td> </tr> <tr> <td>17</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190919T074611_N0208_R135_T35JPL_20190919T105208</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190929T074711_N0208_R135_T35JPL_20190929T100745</td> </tr> <tr> <td>18</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190725T142801_N0208_R053_T20LMR_20190725T175149</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190804T142801_N0208_R053_T20LMR_20190804T175038</td> </tr> <tr> <td>19</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20191101T043931_N0208_R033_T46TDK_20191101T074915</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20191022T043831_N0208_R033_T46TDK_20191022T063301</td> </tr> <tr> <td>20</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20160509T065022_N0202_R020_T41UNV_20160509T065018</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20160519T064632_N0202_R020_T41UNV_20160519T064833</td> </tr> <tr> <td>21</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20191019T012631_N0208_R131_T53LKF_20191019T030531</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20191029T012721_N0208_R131_T53LKF_20191029T040003</td> </tr> <tr> <td>22</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190503T071621_N0207_R006_T38PMB_20190503T092340</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190423T071621_N0207_R006_T38PMB_20190423T093049</td> </tr> <tr> <td>23</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190724T011701_N0208_R031_T56VLM_20190724T031136</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190714T011701_N0208_R031_T56VLM_20190714T031656</td> </tr> <tr> <td>24</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20181020T012651_N0206_R074_T54TXN_20181020T032526</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20181010T012651_N0206_R074_T54TXN_20181010T055606</td> </tr> <tr> <td>25</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20170224T162331_N0204_R040_T16REV_20170224T162512</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20170214T162351_N0204_R040_T16REV_20170214T163022</td> </tr> <tr> <td>26</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190613T032541_N0207_R018_T49UFT_20190613T062257</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190623T032541_N0207_R018_T49UFT_20190623T061953</td> </tr> <tr> <td>27</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190208T011721_N0207_R088_T53KLP_20190208T024521</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190129T011721_N0207_R088_T53KLP_20190129T024501</td> </tr> <tr> <td>28</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190530T184921_N0207_R113_T12VVN_20190530T222535</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190520T184921_N0207_R113_T12VVN_20190520T222900</td> </tr> </tbody> </table> <p>Testing set</p> <table> <tbody> <tr> <td>1</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20150826T084006_N0204_R064_T37UCQ_20150826T084003</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20150905T083736_N0204_R064_T37UCQ_20150905T084002</td> </tr> <tr> <td>2</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20191101T000241_N0208_R030_T56HLH_20191101T012241</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20191111T000241_N0208_R030_T56HLH_20191111T012137</td> </tr> <tr> <td>3</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190711T174911_N0208_R141_T13TEE_20190711T212846</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190701T174911_N0207_R141_T13TEE_20190701T212910</td> </tr> <tr> <td>4</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190201T093221_N0207_R136_T32PRR_20190201T113425</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190211T093121_N0207_R136_T32PRR_20190211T103706</td> </tr> <tr> <td>5</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190314T021601_N0207_R003_T52SCF_20190314T055026</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190304T021601_N0207_R003_T52SCF_20190304T042035</td> </tr> <tr> <td>6</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20180804T045701_N0206_R119_T46VDH_20180804T065907</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20180725T045701_N0206_R119_T46VDH_20180725T065359</td> </tr> <tr> <td>7</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190707T213531_N0207_R086_T05VPJ_20190707T231819</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190627T213531_N0207_R086_T05VPJ_20190628T010801</td> </tr> <tr> <td>8</td> <td>Cloud-free</td> <td>S2A_MSIL1C_20190605T125311_N0207_R052_T24MXV_20190605T160555</td> </tr> <tr> <td> </td> <td>Cloudy</td> <td>S2A_MSIL1C_20190615T125311_N0207_R052_T24MXV_20190615T142536</td> </tr> </tbody> </table>
WHUS2-CD+ dataset for sentinel-2 cloud detection validation
<p>WHUS2-CD+ is a cloud validation detection dataset for Sentinel-2A images. WHUS2-CD+ contains 36 manually labeled cloud masks at 10m resolution and corresponding Sentinel-2A images evenly distributed over China mainland.</p> <p>If you use this dataset for your research, please cite us accordingly:</p> <p>#Reference: </p> <p>[1] J. Li, Z. Wu, Z. Hu, C. Jian, S. Luo, L. Mou, X. Zhu, and M. Molinier, "A lightweight deep learning based cloud detection method for Sentinel-2A imagery fusing multi-scale spectral and spatial features," in IEEE Transactions on Geoscience and Remote Sensing, 2021. <a href="https://doi.org/10.1109/TGRS.2021.3069641">https://doi.org/10.1109/TGRS.2021.3069641</a>.</p> <p>[2] Z. Wu, J. Li, Y. Wang, Z. Hu and M. Molinier, "Self-Attentive Generative Adversarial Network for Cloud Detection in High Resolution Remote Sensing Images," in IEEE Geoscience and Remote Sensing Letters, vol. 17, no. 10, pp. 1792-1796, Oct. 2020. <a href="https://doi.org/10.1109/LGRS.2019.2955071">https://doi.org/10.1109/LGRS.2019.2955071</a>.</p> <p>The training and testing list is (The challenging senes are marked in bold):</p> <table> <tbody> <tr> <td>Training set</td> </tr> <tr> <td>S2A_MSIL1C_20190714T043711_N0208_R033_T46TFN_20190714T073938</td> </tr> <tr> <td>S2A_MSIL1C_20191219T040151_N0208_R004_T47SQU_20191219T055033</td> </tr> <tr> <td>S2A_MSIL1C_20190630T045701_N0207_R119_T45SWC_20190630T080543</td> </tr> <tr> <td>S2A_MSIL1C_20191215T042151_N0208_R090_T46RGV_20191215T065406</td> </tr> <tr> <td>S2A_MSIL1C_20180930T044701_N0206_R076_T45SXR_20180930T074413</td> </tr> <tr> <td>S2A_MSIL1C_20200317T024541_N0209_R132_T51TWM_20200317T053350</td> </tr> <tr> <td>S2A_MSIL1C_20180816T053641_N0206_R005_T44TKK_20180816T093424</td> </tr> <tr> <td>S2A_MSIL1C_20191023T040821_N0208_R047_T47TQF_20191023T074550</td> </tr> <tr> <td>S2A_MSIL1C_20180824T031541_N0206_R118_T50TKL_20180824T061636</td> </tr> <tr> <td>S2A_MSIL1C_20191118T025011_N0208_R132_T50RMN_20191118T071843</td> </tr> <tr> <td>S2A_MSIL1C_20190916T023551_N0208_R089_T50RQS_20190916T042547</td> </tr> <tr> <td>S2A_MSIL1C_20190819T031541_N0208_R118_T49SFU_20190819T065332</td> </tr> <tr> <td>S2A_MSIL1C_20190815T051651_N0208_R062_T44TPN_20190815T090034</td> </tr> <tr> <td>S2A_MSIL1C_20200410T022551_N0209_R046_T51TXG_20200410T042047</td> </tr> <tr> <td>S2A_MSIL1C_20191002T025551_N0208_R032_T50TQQ_20191002T054113</td> </tr> <tr> <td>S2A_MSIL1C_20180429T032541_N0206_R018_T49SCV_20180429T062304</td> </tr> <tr> <td>S2A_MSIL1C_20200506T024551_N0209_R132_T51UWS_20200506T043639</td> </tr> <tr> <td>S2A_MSIL1C_20200325T034531_N0209_R104_T47RQL_20200325T065315</td> </tr> <tr> <td>S2A_MSIL1C_20190928T031541_N0208_R118_T49RBJ_20190928T061248</td> </tr> <tr> <td>S2A_MSIL1C_20180827T032541_N0206_R018_T48RYV_20180827T062627</td> </tr> <tr> <td>S2A_MSIL1C_20200222T030731_N0209_R075_T49QEE_20200222T060244</td> </tr> <tr> <td>S2A_MSIL1C_20180722T030541_N0206_R075_T49RFP_20180722T060550</td> </tr> <tr> <td>S2A_MSIL1C_20180729T025551_N0206_R032_T49RGL_20180729T055945</td> </tr> <tr> <td>S2A_MSIL1C_20200506T024551_N0209_R132_T50SPE_20200506T052918</td> </tr> <tr> <td>Testing set</td> </tr> <tr> <td>S2A_MSIL1C_20180930T030541_N0206_R075_T49QDD_20180930T060706</td> </tr> <tr> <td>S2A_MSIL1C_20191105T023901_N0208_R089_T51STR_20191105T054744</td> </tr> <tr> <td>S2A_MSIL1C_20190812T032541_N0208_R018_T48RXU_20190812T070322</td> </tr> <tr> <td>S2A_MSIL1C_20190602T021611_N0207_R003_T52TES_20190602T042019</td> </tr> <tr> <td>S2A_MSIL1C_20190328T033701_N0207_R061_T49TCF_20190328T071457</td> </tr> <tr> <td>S2A_MSIL1C_20191001T050701_N0208_R019_T45TXN_20191002T142939</td> </tr> <tr> <td>S2A_MSIL1C_20200416T042701_N0209_R133_T46SFE_20200416T074050</td> </tr> <tr> <td>S2A_MSIL1C_20200528T050701_N0209_R019_T44SPC_20200528T082127</td> </tr> <tr> <td><strong>S2A_MSIL1C_20210207T023851_N0209_R089_T52UCU_20210207T040210</strong></td> </tr> <tr> <td><strong>S2A_MSIL1C_20210126T052111_N0209_R062_T44SNE_20210126T063836</strong></td> </tr> <tr> <td><strong>S2A_MSIL1C_20210102T054231_N0209_R005_T43SFB_20210102T065941</strong></td> </tr> <tr> <td><strong>S2A_MSIL1C_20201206T041141_N0209_R047_T47SMV_20201206T053320</strong></td> </tr> </tbody> </table>
Satellite remote sensing dataset of Sentinel-2 for phenology metrics extraction from sites in Bulgaria and France
<p><strong>Site Description:</strong></p> <p>In this dataset, there are seventeen production crop fields in Bulgaria where winter rapeseed and wheat were grown and two research fields in France where winter wheat – rapeseed – barley – sunflower and winter wheat – irrigated maize crop rotation is used. The full description of those fields is in the database "In-situ crop phenology dataset from sites in Bulgaria and France" (doi.org/10.5281/zenodo.7875440).</p> <p> </p> <p><strong>Methodology and Data Description:</strong></p> <p>Remote sensing data is extracted from Sentinel-2 tiles 35TNJ for Bulgarian sites and 31TCJ for French sites on the day of the overpass since September 2015 for Sentinel-2 derived vegetation indices and since October 2016 for HR-VPP products. To suppress spectral mixing effects at the parcel boundaries, as highlighted by Meier et al., 2020, the values from all datasets were subgrouped per field and then aggregated to a single median value for further analysis.</p> <p>Sentinel-2 data was downloaded for all test sites from CREODIAS (https://creodias.eu/) in L2A processing level using a maximum scene-wide cloudy cover threshold of 75%. Scenes before 2017 were available in L1C processing level only. Scenes in L1C processing level were corrected for atmospheric effects after downloading using Sen2Cor (v2.9) with default settings. This was the same version used for the L2A scenes obtained intermediately from CREODIAS. </p> <p>Next, the data was extracted from the Sentinel-2 scenes for each field parcel where only SCL classes 4 (vegetation) and 5 (bare soil) pixels were kept. We resampled the 20m band B8A to match the spatial resolution of the green and red band (10m) using nearest neighbor interpolation. The entire image processing chain was carried out using the open-source Python Earth Observation Data Analysis Library (EOdal) (Graf et al., 2022).</p> <p>Apart from the widely used Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), we included two recently proposed indices that were reported to have a higher correlation with photosynthesis and drought response of vegetation: These were the Near-Infrared Reflection of Vegetation (NIRv) (Badgley et al., 2017) and Kernel NDVI (kNDVI) (Camps-Valls et al., 2021). We calculated the vegetation indices in two different ways: </p> <p>First, we used <strong>B08</strong> as near-infrared (NIR) band which comes in a native spatial resolution of 10 m. <strong>B08</strong> (central wavelength 833 nm) has a relatively coarse spectral resolution with a bandwidth of 106 nm.</p> <p>Second, we used <strong>B8A</strong> which is available at 20 m spatial resolution. <strong>B8A</strong> differs from B08 in its central wavelength (864 nm) and has a narrower bandwidth (21 nm or 22 nm in the case of Sentinel-2A and 2B, respectively) compared to B08.</p> <p> </p> <p>The High Resolution Vegetation Phenology and Productivity (<strong>HR-VPP</strong>) dataset from Copernicus Land Monitoring Service (CLMS) has three 10-m set products of Sentinel-2: vegetation indices, vegetation phenology and productivity parameters and seasonal trajectories (Tian et al., 2021). Both vegetation indices, Normalized Vegetation Index (NDVI) and Plant Phenology (PPI) and plant parameters, Fraction of Absorbed Photosynthetic Active Radiation (FAPAR) and Leaf Area Index (LAI) were computed for the time of Sentinel-2 overpass by the data provider. </p> <p>NDVI is computed directly from B04 and B08 and PPI is computed using Difference Vegetation Index (DVI = B08 - B04) and its seasonal maximum value per pixel. FAPAR and LAI are retrieved from B03 and B04 and B08 with neural network training on PROSAIL model simulations. The dataset has a quality flag product (QFLAG2) which is a 16-bit that extends the scene classification band (SCL) of the Sentinel-2 Level-2 products. A “medium” filter was used to mask out QFLAG2 values from 2 to 1022, leaving land pixels (bit 1) within or outside cloud proximity (bits 11 and 13) or cloud shadow proximity (bits 12 and 14). </p> <p>The <strong>HR-VPP</strong> daily raw vegetation indices products are described in detail in the user manual (Smets et al., 2022) and the computations details of PPI are given by Jin and Eklundh (2014). Seasonal trajectories refer to the 10-daily smoothed time-series of PPI used for vegetation phenology and productivity parameters retrieval with TIMESAT (Jönsson and Eklundh 2002, 2004).</p> <p>HR-VPP data was downloaded through the WEkEO Copernicus Data and Information Access Services (DIAS) system with a Python 3.8.10 harmonized data access (HDA) API 0.2.1. Zonal statistics [’min’, ’max’, ’mean’, ’median’, ’count’, ’std’, ’majority’] were computed on non-masked pixel values within field boundaries with rasterstats Python package 0.17.00.</p> <p> </p> <p>The Start of season date (SOSD), end of season date (EOSD) and length of seasons (LENGTH) were extracted from the annual Vegetation Phenology and Productivity Parameters (<strong>VPP</strong>) dataset as an additional source for comparison. These data are a product of the Vegetation Phenology and Productivity Parameters, see (https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity/vegetation-phenology-and-productivity) for detailed information.</p> <p> </p> <p><strong>File Description:</strong></p> <p>4 datasets:</p> <p>1_senseco_data_S2_B08_Bulgaria_France; 1_senseco_data_S2_B8A_Bulgaria_France; 1_senseco_data_HR_VPP_Bulgaria_France; 1_senseco_data_phenology_VPP_Bulgaria_France</p> <p>3 metadata:</p> <p>2_senseco_metadata_S2_B08_B8A_Bulgaria_France; 2_senseco_metadata_HR_VPP_Bulgaria_France; 2_senseco_metadata_phenology_VPP_Bulgaria_France</p> <p> </p> <p>The dataset files “1_senseco_data_S2_B8_Bulgaria_France” and “1_senseco_data_S2_B8A_Bulgaria_France” concerns all vegetation indices (EVI, NDVI, kNDVI, NIRv) data values and related information, and metadata file “2_senseco_metadata_S2_B08_B8A_Bulgaria_France” describes all the existing variables. Both “1_senseco_data_S2_B8_Bulgaria_France” and “1_senseco_data_S2_B8A_Bulgaria_France” have the same column variable names and for that reason, they share the same metadata file “2_senseco_metadata_S2_B08_B8A_Bulgaria_France”.</p> <p>The dataset file “1_senseco_data_HR_VPP_Bulgaria_France” concerns vegetation indices (NDVI, PPI) and plant parameters (LAI, FAPAR) data values and related information, and metadata file “2_senseco_metadata_HRVPP_Bulgaria_France” describes all the existing variables. </p> <p>The dataset file “1_senseco_data_phenology_VPP_Bulgaria_France” concerns the vegetation phenology and productivity parameters (LENGTH, SOSD, EOSD) values and related information, and metadata file “2_senseco_metadata_VPP_Bulgaria_France” describes all the existing variables.</p> <p> </p> <p><strong>Bibliography</strong></p> <p>G. Badgley, C.B. Field, J.A. Berry, Canopy near-infrared reflectance and terrestrial photosynthesis, Sci. Adv. 3 (2017) e1602244. https://doi.org/10.1126/sciadv.1602244.</p> <p>G. Camps-Valls, M. Campos-Taberner, Á. Moreno-Martínez, S. Walther, G. Duveiller, A. Cescatti, M.D. Mahecha, J. Muñoz-Marí, F.J. García-Haro, L. Guanter, M. Jung, J.A. Gamon, M. Reichstein, S.W. Running, A unified vegetation index for quantifying the terrestrial biosphere, Sci. Adv. 7 (2021) eabc7447. https://doi.org/10.1126/sciadv.abc7447.</p> <p>L.V. Graf, G. Perich, H. Aasen, EOdal: An open-source Python package for large-scale agroecological research using Earth Observation and gridded environmental data, Comput. Electron. Agric. 203 (2022) 107487. https://doi.org/10.1016/j.compag.2022.107487.</p> <p>H. Jin, L. Eklundh, A physically based vegetation index for improved monitoring of plant phenology, Remote Sens. Environ. 152 (2014) 512–525. https://doi.org/10.1016/j.rse.2014.07.010.</p> <p>P. Jonsson, L. Eklundh, Seasonality extraction by function fitting to time-series of satellite sensor data, IEEE Trans. Geosci. Remote Sens. 40 (2002) 1824–1832. https://doi.org/10.1109/TGRS.2002.802519.</p> <p>P. Jönsson, L. Eklundh, TIMESAT—a program for analyzing time-series of satellite sensor data, Comput. Geosci. 30 (2004) 833–845. https://doi.org/10.1016/j.cageo.2004.05.006.</p> <p>J. Meier, W. Mauser, T. Hank, H. Bach, Assessments on the impact of high-resolution-sensor pixel sizes for common agricultural policy and smart farming services in European regions, Comput. Electron. Agric. 169 (2020) 105205. https://doi.org/10.1016/j.compag.2019.105205.</p> <p>B. Smets, Z. Cai, L. Eklund, F. Tian, K. Bonte, R. Van Hoost, R. Van De Kerchove, S. Adriaensen, B. De Roo, T. Jacobs, F. Camacho, J. Sánchez-Zapero, S. Else, H. Scheifinger, K. Hufkens, P. Jönsson, HR-VPP Product User Manual Vegetation Indices, 2022.</p> <p>F. Tian, Z. Cai, H. Jin, K. Hufkens, H. Scheifinger, T. Tagesson, B. Smets, R. Van Hoolst, K. Bonte, E. Ivits, X. Tong, J. Ardö, L. Eklundh, Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe, Remote Sens. Environ. 260 (2021) 112456. https://doi.org/10.1016/j.rse.2021.112456.</p>
Dataset for "Enhancing Cloud Detection in Sentinel-2 Imagery: A Spatial-Temporal Approach and Dataset"
<p>This dataset is built for time-series Sentinel-2 cloud detection and stored in Tensorflow TFRecord (refer to https://www.tensorflow.org/tutorials/load_data/tfrecord).</p> <p>Each file is compressed in 7z format and can be decompressed using Bandzip or 7-zip software.</p> <p><strong>Dataset </strong><strong> </strong><strong>Structure</strong>:</p> <p>Each filename can be split into three parts using underscores. The first part indicates whether it is designated for training or validation ('train' or 'val'); the second part indicates the Sentinel-2 tile name, and the last part indicates the number of samples in this file.</p> <p>For each sample, it includes:</p> <ol> <li>Sample ID;</li> <li>Array of time series 4 band image patches in 10m resolution, shaped as (n_timestamps, 4, 42, 42);</li> <li>Label list indicating cloud cover status for the center \(6\times6\) pixels of each timestamp;</li> <li>Ordinal list for each timestamp;</li> <li>Sample weight list (reserved);</li> </ol> <p>Here is a demonstration function for parsing the TFRecord file:</p> <pre><code>import tensorflow as tf # init Tensorflow Dataset from file name def parseRecordDirect(fname): sep = '/' parts = tf.strings.split(fname,sep) tn = tf.strings.split(parts[-1],sep='_')[-2] nn = tf.strings.to_number(tf.strings.split(parts[-1],sep='_')[-1],tf.dtypes.int64) t = tf.data.Dataset.from_tensors(tn).repeat().take(nn) t1 = tf.data.TFRecordDataset(fname) ds = tf.data.Dataset.zip((t, t1)) return ds keys_to_features_direct = { 'localid': tf.io.FixedLenFeature([], tf.int64, -1), 'image_raw_ldseries': tf.io.FixedLenFeature((), tf.string, ''), 'labels': tf.io.FixedLenFeature((), tf.string, ''), 'dates': tf.io.FixedLenFeature((), tf.string, ''), 'weights': tf.io.FixedLenFeature((), tf.string, '') } # The Decoder (Optional) class SeriesClassificationDirectDecorder(decoder.Decoder): """A tf.Example decoder for tfds classification datasets.""" def __init__(self) -> None: super().__init__() def decode(self, tid, ds): parsed = tf.io.parse_single_example(ds, keys_to_features_direct) encoded = parsed['image_raw_ldseries'] labels_encoded = parsed['labels'] decoded = tf.io.decode_raw(encoded, tf.uint16) label = tf.io.decode_raw(labels_encoded, tf.int8) dates = tf.io.decode_raw(parsed['dates'], tf.int64) weight = tf.io.decode_raw(parsed['weights'], tf.float32) decoded = tf.reshape(decoded,[-1,4,42,42]) sample_dict = { 'tid': tid, # tile ID 'dates': dates, # Date list 'localid': parsed['localid'], # sample ID 'imgs': decoded, # image array 'labels': label, # label list 'weights': weight } return sample_dict # simple function def preprocessDirect(tid, record): parsed = tf.io.parse_single_example(record, keys_to_features_direct) encoded = parsed['image_raw_ldseries'] labels_encoded = parsed['labels'] decoded = tf.io.decode_raw(encoded, tf.uint16) label = tf.io.decode_raw(labels_encoded, tf.int8) dates = tf.io.decode_raw(parsed['dates'], tf.int64) weight = tf.io.decode_raw(parsed['weights'], tf.float32) decoded = tf.reshape(decoded,[-1,4,42,42]) return tid, dates, parsed['localid'], decoded, label, weight t1 = parseRecordDirect('filename here') dataset = t1.map(preprocessDirect, num_parallel_calls=tf.data.experimental.AUTOTUNE) # </code></pre> <p><strong>Class Definition:</strong></p> <ul> <li>0: clear</li> <li>1: opaque cloud</li> <li>2: thin cloud</li> <li>3: haze</li> <li>4: cloud shadow</li> <li>5: snow</li> </ul> <p><strong>Dataset Construction:</strong></p> <p>First, we randomly generate 500 points for each tile, and all these points are aligned to the pixel grid center of the subdatasets in 60m resolution (eg. B10) for consistence when comparing with other products. <br>It is because that other cloud detection method may use the cirrus band as features, which is in 60m resolution. </p> <p>Then, the time series image patches of two shapes are cropped with each point as the center.<br>The patches of shape \(42 \times 42\) are cropped from the bands in 10m resolution (B2, B3, B4, B8) and are used to construct this dataset.<br>And the patches of shape \(348 \times 348\) are cropped from the True Colour Image (TCI, details see sentinel-2 user guide) file and are used to interpreting class labels.</p> <p>The samples with a large number of timestamps could be time-consuming in the IO stage, thus the time series patches are divided into different groups with timestamps not exceeding 100 for every group.</p>
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