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528 results for “Land cover”
CORINE land cover - Catalan land cover taxonomy dictionary for metropolitan Barcelona
<p>This dataset includes Catalan Land Cover categories that are relevant for metropolitan Barcelona and their translation to CORINE land cover categories.</p> <p>This work has been developed for the ERC project <a href="https://urbag.eu/">URBAG</a></p> <p>Original Catalan land cover map: <a href="https://www.creaf.uab.es/mcsc/">https://www.creaf.uab.es/mcsc/ </a></p> <p>CORINE land cover: <a href="https://land.copernicus.eu/pan-european/corine-land-cover">https://land.copernicus.eu/pan-european/corine-land-cover</a></p>
Urban form data for climate modelling: Sydney at 300 m resolution derived from building-resolving and 2 m land cover datasets
<p><strong>Sydney morphology and land surface dataset</strong></p> <p>This dataset for Sydney, Australia, represents land cover, building morphology, vegetation morphology and other parameters appropriate for input into local or mesoscale urban climate models.</p> <p>The dataset is provided in netCDF4 and GeoTiff formats.</p> <p>Associated manuscript:</p> <blockquote> <p><a href="https://doi.org/10.3389/fenvs.2022.866398">A transformation in city-descriptive input data for urban climate models</a></p> </blockquote> <p>Citation for the open dataset:<br> - Lipson, M., Nazarian, N., Hart, M. A., Nice, K. A., and Conroy, B.: Urban form data for climate modelling: Sydney at 300 m resolution derived from building-resolving and 2 m land cover datasets (v1.01), <a href="https://doi.org/10.5281/zenodo.6579061">https://doi.org/10.5281/zenodo.6579061</a>, 2022.</p> <p>Citation for the associated manuscript:<br> - Lipson, M. J., Nazarian, N., Hart, M. A., Nice, K. A., and Conroy, B.: A Transformation in City-Descriptive Input Data for Urban Climate Models, Frontiers in Environmental Science, 10, <a href="https://doi.org/10.3389/fenvs.2022.866398">https://doi.org/10.3389/fenvs.2022.866398</a>, 2022.</p> <p>Location of associated processing code:<br> - <a href="https://github.com/matlipson/geoscape_processing_public.git">https://github.com/matlipson/geoscape_processing_public.git</a></p> <p><strong>Acknowledgments</strong></p> <p>We gratefully acknowledge the Australian Urban Research Infrastructure Network (AURIN) and Geoscape Australia for <br> providing the datasets necessary for this study, drawing on Geoscape Buildings, Surface Cover and Trees datasets, <br> © Geoscape Australia, 2020: https://geoscape.com.au/legal/data-copyright-and-disclaimer/. <br> This research was supported by the Australian Research Council (ARC) Centre of Excellence for Climate System Science <br> (grant CE110001028), the ARC Centre of Excellence for Climate Extremes (grant CE170100023). </p> <p> </p>
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>
Refined Land cover for Beijing, Shanghai, Ningbo in China and Paris Region, Velika Gorica, Aarhus in Europe under different scenarios in 2030
<p>Europe and China Refined Land Cover 2030 (ECRLC2030) was derived from historical landcover observations, natural geographical, location, and socio-economic factors and the Conversion of Land Use and its Effects at Small Regional Extent model (CLUE-S). With a spatial resolution of 60m, landcover under three different scenarios were simulated: the business-as-usual scenario (BAU), the market-liberal scenario (MLS), and the ecological protection scenario (EPS).</p>
diFUME Land Cover V0.2
<p>Description:</p> <p>Land Cover map (V0.2) of Basel for diFUME project at three different levels of information. Level 1 information originates from the Official survey of Basel-Stadt (<a href="http://www.gva.bs.ch">http://www.gva.bs.ch</a>). Level 2 aggregates the information of Level 1 into 8 broad categories. Level 3 includes the information of tree canopies and crown, derived by airborne Lidar data (2018 campaign) made available by the civil engineering office of Basel-Stadt (<a href="https://www.tiefbauamt.bs.ch/">https://www.tiefbauamt.bs.ch/</a>) and classifies buildings to Commercial/Industrial according to building type information (<a href="http://www.geo.bs.ch">http://www.geo.bs.ch</a>). Additionally, road type classification is available according to a city map and the vehicle traffic zones (<a href="http://www.mobilitaet.bs.ch">http://www.mobilitaet.bs.ch</a>).</p> <p> </p> <p>Data specifications:</p> <p>CRS: EPSG:32632 - WGS 84 / UTM zone 32N - Projected</p> <p>Spatial Extent: 392120.0,5266860.0 : 395160.0,5269840.0</p> <p>Temporal Extent: 2019</p> <p>Units: meters</p> <p>Width: 3040</p> <p>Height: 2980</p> <p>Bands: 1</p> <p>Pixel Size: 1,-1</p> <p>Data type: Float32 - Thirty two bit floating point</p> <p>GDAL Driver Description: GTiff</p> <p>GDAL Driver Metadata: GeoTIFF</p> <p> </p> <p> </p> <p>Legend:</p> <p> </p> <p>Level 1:</p> <p>0 Buildings</p> <p>1 Tanks</p> <p>2 Road</p> <p>3 Pavement</p> <p>4 Paved surfaces</p> <p>5 Train lines</p> <p>6 Tram lines</p> <p>8 Water</p> <p>9 Port area</p> <p>10 Industrial area</p> <p>11 Paved surfaces</p> <p>12 Sport facilities</p> <p>13 Paved surfaces</p> <p>14 Meadow</p> <p>17 Gardens</p> <p>18 Parks</p> <p>19 Graveyard</p> <p>20 Community gardens</p> <p>21 Zoo</p> <p>22 Sport facilities</p> <p>24 Free lands</p> <p>25 Agricultural lands</p> <p>27 Water</p> <p>29 Forest</p> <p>32 Pervious surfaces</p> <p> </p> <p>Level 2:</p> <p>0 Buildings</p> <p>2 Roads</p> <p>3 Pavements</p> <p>4 Paved surfaces</p> <p>5 Train lines</p> <p>8 Water</p> <p>29 Forest</p> <p>33 Soil/vegetation</p> <p> </p> <p>Level 3:</p> <p>0 Buildings</p> <p>2 Roads</p> <p>3 Pavements</p> <p>4 Paved surfaces</p> <p>5 Train lines</p> <p>8 Water</p> <p>10 Trees</p> <p>33 Soil/low vegetation</p> <p>35 Commercial/Industrial 50 %</p> <p>36 Commercial/Industrial 100 %</p> <p> </p> <p>Road Classification:</p> <p>1 Settlement oriented roads</p> <p>2 Main roads</p> <p>3 Main collecting roads</p> <p>4 Other roads/paths</p> <p>5 Meeting areas (20 km/h)</p> <p>6 Tempo 30 (30 km/h)</p> <p>0 Nodata</p>
A Map of Land Use and Land Cover in Southern Malawi Derived from Sentinel-2 Data (2023)
<h3><strong>Overview</strong></h3> <p>The land use and land cover map comprises the Mulanje and Phalombe districts, in Southern Malawi. It includes five classes: forest, natural vegetation, cropland, wetland, and other lands. The map is derived from Sentinel-2 mosaics, resulting in a spatial resolution of 10 meters, for 2023. </p> <p> </p> <h3><strong>Map Accuracy</strong></h3> <p>The land use and land cover map achieves an overall accuracy of 89%. Details of user and producer accuracies are provided in Table 1.</p> <p>Table 1: Land use and land cover classification validation,including overall, producer (PA) and user (UA) accuracies values for each class.</p> <div> <table> <tbody> <tr> <td> <p><strong>Class </strong></p> </td> <td> <p><strong>Producer Accuracy</strong></p> </td> <td> <p><strong>User Accuracy</strong></p> </td> </tr> <tr> <td> <p>Cropland</p> </td> <td> <p> 93%</p> </td> <td> <p> 85%</p> </td> </tr> <tr> <td> <p>Wetland</p> </td> <td> <p> 100%</p> </td> <td> <p> 100%</p> </td> </tr> <tr> <td> <p>Other Lands</p> </td> <td> <p> 90%</p> </td> <td> <p> 95%</p> </td> </tr> <tr> <td> <p>Forest</p> </td> <td> <p> 79%</p> </td> <td> <p> 90%</p> </td> </tr> <tr> <td> <p>Natural Vegetation</p> </td> <td> <p> 90%</p> </td> <td> <p> 90%</p> </td> </tr> <tr> <td> <p><strong>Overall Accuracy</strong></p> </td> <td><br> <p><strong> 89%</strong></p> </td> </tr> </tbody> </table> </div> <h3> </h3> <h3><strong>Files descripion</strong></h3> <ul> <li>MLW_Sentinel_LULC_2023.tif / .qml: land use and land cover map and QGIS style file</li> <li>training_samples.gpkg: training samples with class labels</li> <li>validation_samples.gpkg: validation samples with class labels</li> </ul>
Soil organic carbon stock (0–30 cm) in kg/m2 time-series 2001–2015 based on the land cover changes
<p>Estimated SOC loss based on the European Space Agency (ESA) Climate Change Initiative (ESACCI-LC) land cover maps 2001–2015. This only shows estimated SOC loss (in kg/m2) as a result of change in land use / land cover (assuming standard change factors based on the literature and IPCC reports). Methodology produced for the purpose of the Land Degradation Neutrality (UNCCD) project. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil/LDN">here</a></strong>. Antartica is not included.</p> <p>To access and visualize maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>organic.carbon.stock = variable: soil organic carbon stock in kg/m2,</li> <li>msa.kgm2 = determination method: derived from carbon content, bulk density and coarse fragments,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..30cm = vertical reference: standard layer 0-30 cm below surface,</li> <li>2014 = time reference: year 2014,</li> <li>v0.2 = version number: 0.2,</li> </ul>
Navigating deep learning strategies for large-area land cover mapping using very-high-resolution imagery in Senegal: Validation Data
<p><span><span>R</span><span>apid</span><span> advances in deep learning</span><span> for</span> <span>land cover </span><span>classification of </span><span>trees, shrubs and </span><span>very small</span> <span>agricultur</span><span>al</span> <span>fields</span> <span>using</span> <span>very high</span><span>-</span><span>resolution satellite </span><span>data </span><span>(< 2 m</span><span>)</span><span>,</span><span> has tremendous potential</span> <span>for resolving </span><span>current</span><span> challenges </span><span>in </span><span>quantifying</span> <span>land cover </span><span>change </span><span>in</span> <span>sub-</span><span>Saharan</span> <span>African (SSA</span><span>)</span><span>,</span> <span>due to</span> <span>growing </span><span>demand for food resources</span><span>.</span> <span>We</span> <span>conducted experiments </span><span>with</span><span> different training strategies for scaling up </span><span>UNet</span> <span>convolutional neural network </span><span>models for regional land cover mapping with multispectral </span><span>WorldView</span><span> (WV</span><span>)</span><span>-2 and –3,</span><span> imagery</span><span> in</span><span> three distinct regions of Senegal </span><span>which</span> <span>has</span><span> complex </span><span>seasonal wet/dry conditions and </span><span>cropland-savanna mosaics. </span></span></p> <p>The validation exercise of this research consisted in validating more than 70,000 km<sup>2</sup> across Senegal. The infrastructure was setup in the NASA SMCE system with a total of twelve George Mason University (GMU) students participating as operators. These operators validated more than 59 WV-2 and -3 images, each consisting of 200 stratified points in 5,000 x 5,000-pixel images. This effort resulted in a total of ~35,000 aggregated observations that are available through the eo-validation API for public consumption. Each validation point from this dataset has three individual observations.</p>
Associating Land Cover Changes with Climate Sensitive Infection in Fennoscandia, as part of the CLINF project: Example on Tick-Borne Diseases
<p>The data was used as part of the IJERPH article below. The GeoJSON and shapefile ZIP archive are two versions of the same geometries to represent geographically the districts whole of Fennoscandia and the Russian districts of Leningrad, St Petersburg, Vologda, Arkhangelsk, Nenetsia, Murmansk, Karelia, and Komi, making up 69 districts used for the analysis.</p> <p>Leibovici DG, Bylund H, Björkman C, Tokarevich N, Thierfelder T, Evengård B, Quegan S (2021). Associating Land Cover Changes with Patterns of Incidences of Climate Sensitive Infections: An Example on Tick-Borne Diseases in the Nordic Area. <strong><em>International Journal of Environmental Research and Public Health, 18(20):10963. <a href="https://doi.org/10.3390/ijerph182010963">doi:10.3390/ijerph182010963</a></em></strong></p> <p>Special Issue: <a href="https://www.mdpi.com/journal/ijerph/special_issues/Climate-Change_Effects">https://www.mdpi.com/journal/ijerph/special_issues/Climate-Change_Effects</a></p> <p> </p>
High resolution land cover 2015 Aarhus, Denmark
<p><strong>Description</strong></p> <p>This dataset provides land-cover and land-use information at a 20cm resolution for the municipality of Aarhus, Denmark. It depicts the status for the year 2015, containing 23 thematic classes.</p> <p><strong>Spatial reference</strong><br> All data is projected in ETRS 1989 UTM Zone 32N (EPSG:25832)</p> <p><strong>Related publication</strong><br> J. M. Knopp, G. Levin and E. Banzhaf, "Aerial Data Analysis for Integration Into a Green Cadastre—An Example From Aarhus, Denmark," in <em>IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</em>, vol. 16, pp. 6545-6555, 2023, doi: <a href="https://ieeexplore.ieee.org/document/10168752">10.1109/JSTARS.2023.3289218</a>.</p> <p><strong>Class Codec</strong></p> <table> <tbody> <tr> <td> <p><strong>Class</strong></p> </td> <td> <p><strong>Vector </strong></p> <p><strong>NumCodec</strong></p> <p><strong>(16bit)</strong></p> </td> <td> <p><strong>Raster</strong></p> <p><strong>NumCodec</strong></p> <p><strong>(8bit)</strong></p> </td> </tr> <tr> <td> <p> </p> </td> <td> <p> </p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Building</p> </td> <td> <p>100</p> </td> <td> <p>10</p> </td> </tr> <tr> <td> <p>0 Lowest rise building</p> </td> <td> <p>110</p> </td> <td> <p>11</p> </td> </tr> <tr> <td> <p>1 Low rise building</p> </td> <td> <p>120</p> </td> <td> <p>12</p> </td> </tr> <tr> <td> <p>2 Mid rise building</p> </td> <td> <p>130</p> </td> <td> <p>13</p> </td> </tr> <tr> <td> <p>3 High rise building</p> </td> <td> <p>140</p> </td> <td> <p>14</p> </td> </tr> <tr> <td> <p>4 Highest rise building</p> </td> <td> <p>150</p> </td> <td> <p>15</p> </td> </tr> <tr> <td> <p>Mineral surface</p> </td> <td> <p>210</p> </td> <td> <p>21</p> </td> </tr> <tr> <td> <p>Bare soil</p> </td> <td> <p>220</p> </td> <td> <p>22</p> </td> </tr> <tr> <td> <p>Artificial grass</p> </td> <td> <p>230</p> </td> <td> <p>23</p> </td> </tr> <tr> <td> <p>Grass</p> </td> <td> <p>310</p> </td> <td> <p>31</p> </td> </tr> <tr> <td> <p>Shrub round</p> </td> <td> <p>410</p> </td> <td> <p>41</p> </td> </tr> <tr> <td> <p>Shrub linear</p> </td> <td> <p>420</p> </td> <td> <p>42</p> </td> </tr> <tr> <td> <p>Evergreen</p> </td> <td> <p>510</p> </td> <td> <p>51</p> </td> </tr> <tr> <td> <p>Deciduous</p> </td> <td> <p>520</p> </td> <td> <p>52</p> </td> </tr> <tr> <td> <p>Lake</p> </td> <td> <p>610</p> </td> <td> <p>61</p> </td> </tr> <tr> <td> <p>River</p> </td> <td> <p>620</p> </td> <td> <p>62</p> </td> </tr> <tr> <td> <p>Sea</p> </td> <td> <p>630</p> </td> <td> <p>63</p> </td> </tr> <tr> <td> <p>Undergrowth</p> </td> <td> <p>710</p> </td> <td> <p>71</p> </td> </tr> <tr> <td> <p>Agriculture, intensive temporary crops</p> </td> <td> <p>810</p> </td> <td> <p>81</p> </td> </tr> <tr> <td> <p>Agriculture, intensive permanent crops</p> </td> <td> <p>820</p> </td> <td> <p>82</p> </td> </tr> <tr> <td> <p>Agriculture, extensive</p> </td> <td> <p>830</p> </td> <td> <p>83</p> </td> </tr> <tr> <td> <p>unclassified</p> </td> <td> <p>999</p> </td> <td> <p>99</p> </td> </tr> <tr> <td> <p>NonAOI</p> </td> <td> <p>999</p> </td> <td> <p>99</p> </td> </tr> </tbody> </table>
OEMC Hackathon 2023: EU Land Cover Classification Dataset
<p>Dataset organized by the <a href="https://earthmonitor.org/">Open-Earth-Monitor (OEMC) project</a> within the context of <a href="http://www.kaggle.com/competitions/oemc-hackathon-eu-land-cover-classification/overview">Hackathon 2023</a>.</p> <p>The dataset (both train and test) was produced by stratified sampling of the <strong>ground-truth</strong> data provided by LUCAS Survey, funded by the European Commission. The target land cover considered <strong>level-3</strong> classes from the harmonized legend, resulting in <strong>72 classes</strong> distributed over <strong>5 years </strong>(<code>2006</code>, <code>2009</code>, <code>2012</code>, <code>2015</code>, <code>2018</code>):</p> <p>All samples were overlaid with <strong>416</strong> raster spatial layers, including satellite (spectral bands and indices) and temperature images (land surface temperature), climate images (precipitation, air temperature), accessibility and distance maps (highways, water bodies, burned areas), digital terrain model (slope and elevation) and other existing maps (population count and snow covering). The result values were organized in columns, one for each spatial layers, which combined represent the feature space available for ML modeling.</p> <p><strong>Column names:</strong></p> <p>The columns are formed by six metadata fields separated by <code>_</code>:</p> <ul> <li>Example: <strong>red_landsat.glad.ard_p50_30m_jun25_sep12</strong></li> <li>Metadata fields: <ul> <li>F1 - Variable name: <strong>red</strong></li> <li>F2 - Variable procedure including product name: <strong>landsat.glad.ard</strong></li> <li>F3 - Position in the probability distribution: <strong>p50</strong></li> <li>F4 - Spatial resolution: <strong>30m</strong></li> <li>F5 - Start date: <strong>jun25</strong></li> <li>F6 - End date: <strong>sep12</strong></li> </ul> </li> </ul> <p><strong>Column description:</strong></p> <p>All the columns can be aggregated in six thematic groups according to F1 and F2:</p> <ul> <li><strong>Satellite images (spectral reflectance & vegetation indices):</strong> <ul> <li><code>blue_landsat.glad.ard_{..}</code>: Quarterly time-series of Landsat blue band (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> <li><code>blue_mod13q1_{..}</code>: Monthly time-series of MOD13Q1 blue band (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>evi_mod13q1.stl.trend.ols.alpha_{..}</code>: Alpha coefficient / intercept (derived by <a href="https://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.OLS.html">OLS</a>) over the deseasonalized monthly time-series of MOD13Q1 Enhanced Vegetation Index (EVI) index (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>evi_mod13q1.stl.trend.ols.beta_{..}</code>: Beta coefficient / trend (derived by <a href="https://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.OLS.html">OLS</a>) over the deseasonalized monthly time-series of MOD13Q1 Enhanced Vegetation Index (EVI) index (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>evi_mod13q1.stl.trend_{..}</code>: Deseasonalized monthly time-series (trend component of <a href="https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.STL.html#statsmodels.tsa.seasonal.STL">STL</a>) for MOD13Q1 Enhanced Vegetation Index (EVI) index (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>evi_mod13q1_{..}</code>: Monthly time-series of MOD13Q1 Enhanced Vegetation Index (EVI) index (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>green_landsat.glad.ard_{..}</code>: Quarterly time-series of Landsat green band (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> <li><code>mir_mod13q1_{..}</code>: Monthly time-series of MOD13Q1 mid-infrared band (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>ndvi_mod13q1_{..}</code>: Monthly time-series of MOD13Q1 normalized vegetation index (NDVI) (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>nir_landsat.glad.ard_{..}</code>: Quarterly time-series of Landsat near-infrared band (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> <li><code>nir_mod13q1_{..}</code>: Monthly time-series of MOD13Q1 near-infrared band (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>red_landsat.glad.ard_{..}</code>: Quarterly time-series of Landsat red band (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> <li><code>red_mod13q1_{..}</code>: Monthly time-series of MOD13Q1 red band (<a href="https://lpdaac.usgs.gov/products/mod13q1v006/">EarthData</a>)</li> <li><code>swir1_landsat.glad.ard_{..}</code>: Quarterly time-series of Landsat short-wave infrared-1 band (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> <li><code>swir2_landsat.glad.ard_{..}</code>: Quarterly time-series of Landsat short-wave infrared-1 band (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> </ul> </li> <li><strong>Temperature images:</strong> <ul> <li><code>lst_mod11a2.daytime_{..}</code>: Monthly time-series of MOD13Q1 day time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.daytime.{month}_{..}</code>: Long-term monthly aggregation (2000—2022) for MOD13Q1 day time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.daytime.trend_{..}</code>: Deseasonalized monthly time-series (trend component of <a href="https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.STL.html#statsmodels.tsa.seasonal.STL">STL</a>) for MOD13Q1 day time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.daytime.trend.ols.alpha_{..}</code>: Alpha coefficient / intercept (derived by <a href="https://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.OLS.html">OLS</a>) over the deseasonalized monthly time-series of MOD13Q1 day time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.daytime.trend.ols.beta_{..}</code>: Beta coefficient / trend (derived by <a href="https://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.OLS.html">OLS</a>) over the deseasonalized monthly time-series of MOD13Q1 day time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.nighttime_{..}</code>: Monthly time-series of MOD13Q1 night time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.nighttime.{month}_{..}</code>: Long-term monthly aggregation (2000—2022) for MOD13Q1 day time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.nighttime.trend_{..}</code>: Deseasonalized monthly time-series (trend component of <a href="https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.STL.html#statsmodels.tsa.seasonal.STL">STL</a>) for MOD13Q1 night time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.nighttime.trend.ols.alpha_{..}</code>: Alpha coefficient / intercept (derived by <a href="https://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.OLS.html">OLS</a>) over the deseasonalized monthly time-series of MOD13Q1 night time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>lst_mod11a2.nighttime.trend.ols.beta_{..}</code>: Beta coefficient / trend (derived by <a href="https://www.statsmodels.org/devel/generated/statsmodels.regression.linear_model.OLS.html">OLS</a>) over the deseasonalized monthly time-series of MOD13Q1 night time land surface temperature (<a href="https://lpdaac.usgs.gov/products/mod11a2v006/">EarthData</a>)</li> <li><code>thermal_landsat.glad.ard_{..}</code>: Quarterly time-series of Landsat thermal band (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> </ul> </li> <li><strong>Climate layers:</strong> <ul> <li><code>accum.precipitation_chelsa.annual_{..}</code>: Accumulated precipitation over the entire year according to CHELSA timeseries in <code>mm</code> of water (<a href="https://doi.org/10.1038/sdata.2017.122">Karger et al., 2017</a>)</li> <li><code>accum.precipitation_chelsa.annual.3years.dif_{..}</code>: 3-years difference considering the yearly accumulated precipitation according to CHELSA timeseries in <code>mm</code> of water (<a href="https://doi.org/10.1038/sdata.2017.122">Karger et al., 2017</a>)</li> <li><code>accum.precipitation_chelsa.annual.log.csum_{..}</code>: Cumulative sum, in logarithmic space, consdering the yearly accumulated precipitation according to CHELSA timeseries (<a href="https://doi.org/10.1038/sdata.2017.122">Karger et al., 2017</a>)</li> <li><code>accum.precipitation_chelsa.montlhy_{..}</code>: Accumulated precipitation for each month according to CHELSA timeseries in <code>mm</code> of water (<a href="https://doi.org/10.1038/sdata.2017.122">Karger et al., 2017</a>)</li> <li><code>bioclim.var_chelsa.{variable_code}_{..}</code>: Bioclimatic variables derived variables from the monthly mean, max, mean temperature, and mean precipitation values. For <code>variable_code</code> descriptions see <a href="https://chelsa-climate.org/bioclim/">chelsa-climate.org</a> (<a href="https://doi.org/10.1038/sdata.2017.122">Karger et al., 2017</a>)</li> </ul> </li> <li><strong>Accessibility & distance maps:</strong> <ul> <li><code>accessibility.to.ports_map.ox.{variable_code}_{..}</code>: Time-required to access ports of different size according to <a href="https://doi.org/10.1038/s41597-019-0265-5">Nelson et al., 2019</a></li> <li><code>burned.area.distance_global.fire.atlas_{..}</code>: Distance to burned areas mapped by <a href="https://doi.org/10.3334/ORNLDAAC/1642">Global Fire Atlas</a></li> <li><code>cost.distance.to.coast_gedi.grass.gis_{..}</code>: Cumulative cost of moving (derived by <a href="https://grass.osgeo.org/grass83/manuals/r.cost.html">r.cost</a>) to the coast</li> <li><code>road.distance_osm.highways.high.density_{..}</code>: Distance to high density of roads according to <a href="https://www.openstreetmap.org/#map=8/52.154/5.295">OpenStreetMap</a></li> <li><code>road.distance_osm.highways.low.density_{..}</code>: Distance to low density of roads according to <a href="https://www.openstreetmap.org/#map=8/52.154/5.295">OpenStreetMap</a></li> <li><code>water.distance_glad.interanual.dynamic.classes_{..}</code>: Distance to permanent / seasonal water bodies according to<br> <a href="https://doi.org/10.1016/j.rse.2020.111792">Pickens et al., 2020</a></li> </ul> </li> <li><strong>Digital terrain model (DTM):</strong> <ul> <li><code>elev.lowestmode_gedi.eml_{..}</code>: Mean estimate of the terrain elevation in <code>dm</code> filtered using <a href="https://saga-gis.sourceforge.io/saga_tool_doc/6.2.0/grid_filter_1.html">SAGA GIS Gaussian filter</a> (<a href="https://doi.org/10.7717/peerj.15478">Witjes et al., 2023</a>)</li> <li><code>slope.percent_gedi.eml_{..}</code>: Mean slope in <code>%</code> derived from terrain elevation ([Witjes et al., 2023]</li> </ul> </li> <li><strong>Other existing maps:</strong> <ul> <li><code>pop.count_ghs.jrc_{..}</code>: Annual time-series of population count in number of people mapped by <a href="https://data.jrc.ec.europa.eu/dataset/2ff68a52-5b5b-4a22-8f40-c41da8332cfe">Schiavina et al., 2023</a></li> <li><code>snow.duration_global.snowpack_{..}</code>: Annual duration of snow occurrence mapped by <a href="https://www.dlr.de/eoc/desktopdefault.aspx/tabid-8297/14218_read-37938/">Global SnowPack</a></li> </ul> </li> </ul> <p><strong>Files</strong></p> <ul> <li><strong>train.csv</strong>: Training set with 42,237 rows and 420 columns, including sample id (<code>sample_id</code> - index column), land cover code (<code>land_cover</code>), land cover label (<code>land_cover_label</code>), reference year (<code>year</code>) and 416 features / covariates</li> <li><strong>test.csv</strong>: Test set with 42,271 rows and 418 columns, including sample id (<code>sample_id</code> - index column), reference year (<code>year</code>) and 416 features / covariates</li> <li><strong>sample_submission.csv</strong>: a sample submission file with 42,271 rows and 2 columns, including sample id (<code>sample_id</code> - index column) and predicted land cover code (<code>land_cover</code>)</li> </ul>
Supervised land cover classification using Google Earth Engine in Córdoba, Argentina, 2018-2020
Land cover information is critical to scientific, economic, and public policy-making. There is a high demand for accurate and timely land cover information that affects the accuracy of all subsequent applications. The availability of Google Earth Engine (GEE), which derives temporal aggregation methods from time-series images (i.e., the use of metrics such as mean or median), has also enabled optimization of computation time, such as managing large amounts of data to obtain more accurate results. Our objective was to obtain a land cover map for the northwest of the province of Córdoba, Argentina. The study was carried out in rural communities that belong to the departments of Cruz del Eje and Ischilín, northwest of Córdoba, and have different degrees of intervention in the land cover. Sentinel 2 Level 2A images were acquired for the study area. Images available from January 1, 2018, to December 31, 2020, were sampled. To create a thematic map, the median value was calculated for the sample of images from the selected time interval. Finally, the Normalized Difference Vegetation Index (NDVI) was calculated and added to the total bands of the median image. Training polygons were placed there considering the visual features in the median image. The Random Forest algorithm was used as the classification method. To verify the quality of the classified map, a list of 97,753 verification pixels was obtained. In addition, a confusion matrix was created to collect the conflicts that arise between categories, and the precision and kappa coefficient was calculated to define the quality of the map obtained. Image acquisition, preprocessing, and analysis were performed on the Google Earth Engine platform. Thematic maps with eight classes were obtained, with a total area of 719880 ha. The confusion matrix showed an overall precision of 99.26% and a corrected kappa index of 0.99, the classes were correctly classified by the algorithm.
Water chemistry, bedrock geology, and land cover data for Pennsylvania headwater streams: 2007-2022.
This data package contains all data necessary to run random forest and regression analyses featured in the article: 'Influence of bedrock geology on headwater stream pH' by G. Moyer, K. Frantz, and M. Shank. The data include water chemistry, land cover, and geologic formations for 271 headwater streams in Pennsylvania. Data from two previously published papers are also included: Ponce et al. (1979) and Lynch and Dise (1985), which were used as validation datasets.
A Land-use/Land Cover Classification of Baltimore City in 1953
Land-use and land cover classifications are typically created using automated methods to analyze modern, spatially explicit color aerial imagery. However, creating classifications from black and white historical aerial imagery presents a number of challenges that require a combination of more traditional, manual techniques and approaches. A georectified mosaic of 113 aerial images was digitized in ArcGIS to create a land-use/land cover classification. The analyzed area covered 700 km2 (270 mi2) including all of Baltimore City, and a portion of Baltimore County immediately surrounding the city. A combination of 8 land-use and land cover classes were used: Agriculture, Barren, Built (Other), Forest, Grass/Shrubland, Industrial, Residential, and Water. This geospatial data set captures an ecologically and socially important moment in the post-war history of the city. It can be used to examine relationships between property ownership and forest patch dynamics across time. These insights may help inform future environmental planning, conservation, management, and stewardship goals for Baltimore City forest patches, and other cities throughout the region.
Urban-Rural Temperature Data-relation between land-cover and the Urban Heat Island in San Juan, Puerto Rico
Our objective in this study is to quantify the UHI created by the San Juan Metropolitan Area over space and time using temperature data collected by mobile and fixed-station measurements. We used the fixed-station measurements to examine the relation between average temperature at a given location and the density of vegetation located upwind. We then regressed temperatures against regional land-cover to predict future temperature with projected land-cover change. Our data show the existence of a nocturnal UHI, with average nighttime urban-rural temperature differences (ΔTU-R) of up to 3.02°C. Each of the stations listed in this excel file were used to calculate the urban heat island created by the San Juan Metropolitan Area. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
MCR LTER: Coral Reef: 2018 land cover map of Moorea, French Polynesia
Using a collection of imagery from June-September 2018 taken by the Worldview-3 satellite, land cover was classified for the island of Mo’orea, French Polynesia. A deep learning pixel classification model was trained for each of four separate dates of image collection when clouds were sparse over the island. The model was trained at the native resolution of the imagery (<2m pixels). Training data included the multispectral WV-3 bands in addition to derived bands that index vegetation productivity (NDVI), vegetation texture (NDVI IDM), and water cover (NDWI). A consensus land cover map was generated from model predictions across the four sets of imagery.
Green Lakes Valley land cover classification, Niwot Ridge LTER, Colorado
Land cover data generated by Don Cline (graduate student, CU Boulder Geography), as part of suite of spatial maps made for Green Lakes Valley (see Williams et al. 1999).
Copernicus Global Land Service: Land Cover 100m: epoch 2018: Africa demo (deprecated)
<p><strong><em>This demo dataset over Africa is deprecated. Please see <a href="https://doi.org/10.5281/zenodo.3518037">this global dataset</a> instead.</em></strong></p> <p>Demonstration land cover maps over Africa at 100m resolution for epoch year 2018, from the global component of the Copernicus Land Service and derived from PROBA-V satellite observations.</p> <p>The maps include the main discrete classification (23 classes aligned with UN-FAO's LCCS), a set of cover fractions (%) for the 10 main classes and additional quality layers (e.g. density of input data).</p> <p>For the near-real time (nrt) epoch 2018, the classifier and regression models of base year 2015 are used, and the time window of the classified metrics covers one full year prior (2017) and three months pastor (Jan-March 2019) data. The nrt map can then be supplied in the fourth month after the most recent completed calendar year, and is updated (consolidated) afterwards by using a full year of paster data (when epoch 2019-nrt is produced, epoch 2018 is consolidated).</p> <p>The layers with the probability of the discrete classification and the standard deviation of the cover fractions are only provided for the base year (epoch 2015). The Change Consistency Layer that checks consistency between classifier and break detection, is only available for this near-real time epoch</p> <p> </p> <p><a href="https://africa.lcviewer.vito.be/2018">View the maps and area statistics</a></p> <p><a href="https://land.copernicus.eu/global/documents/lcc100/all/pum">Product User Manual</a></p> <p><a href="https://land.copernicus.eu/global/products/lc">More land cover change product information and documentation</a></p>
Copernicus Global Land Service: Land Cover 100m: epoch 2017: Africa demo (deprecated)
<p><strong><em>This demo dataset over Africa is deprecated. Please see <a href="https://doi.org/10.5281/zenodo.3518035">this global dataset</a> instead.</em></strong></p> <p>emonstration land cover maps over Africa at 100m resolution for epoch year 2017, from the global component of the Copernicus Land Service and derived from PROBA-V satellite observations.</p> <p>The maps include the main discrete classification (23 classes aligned with UN-FAO's LCCS), a set of cover fractions (%) for the 10 main classes and additional quality layers (e.g. density of input data).</p> <p>For the consolidated epoch 2017, the classifier and regression models of base year 2015 are used, and the time window of the classified metrics covers one full year prior (2016) and pastor (2018) data. The layers with the probability of the discrete classification and the standard deviation of the cover fractions are only provided for the base year (epoch 2015). The Change Consistency Layer that checks consistency between classifier and break detection, is only available for the most recent (near-real time) year (epoch 2018).</p> <p> </p> <p><a href="https://africa.lcviewer.vito.be/2017">View the maps and area statistics</a></p> <p><a href="https://land.copernicus.eu/global/documents/lcc100/all/pum">Product User Manual</a></p> <p><a href="https://land.copernicus.eu/global/products/lc">More land cover change product information and documentation</a></p> <p> </p>
Copernicus Global Land Service: Land Cover 100m: epoch 2015: Africa demo (deprecated)
<p><strong><em>This demo dataset over Africa is deprecated. Please see <a href="https://doi.org/10.5281/zenodo.3243508">this global dataset</a> instead.</em></strong></p> <p>Demonstration land cover maps over Africa at 100m resolution for epoch year 2015, from the global component of the Copernicus Land Service and derived from PROBA-V satellite observations.</p> <p>The maps include the main discrete classification (23 classes aligned with UN-FAO's LCCS), a set of cover fractions (%) for the 10 main classes and additional quality layers (e.g. density of input data).</p> <p>As a base year, the classification and regression models for 2015 are saved for re-use in subsequent consolidated (with full year prior and pastor observations) and near-real time years (with full year prior and 3 months pastor data). The layers with the probability of the discrete classification and the standard deviation of the cover fractions are only provided for this base epoch. The Change Consistency Layer that checks consistency between classifier and break detection, is only available for the most recent (near-real time) epoch (2018).</p> <p> </p> <p><a href="https://africa.lcviewer.vito.be/2015">View the maps and area statistics</a></p> <p><a href="https://land.copernicus.eu/global/documents/lcc100/all/pum">Product User Manual</a></p> <p><a href="https://land.copernicus.eu/global/products/lc">More land cover change product information and documentation</a></p> <p> </p>
ScienceDex guides
Understand access before you commit
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.