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5,023 results for “landing”
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>
Capturing land cover and land use with street level imagery
<p>This dataset, collected in September 2018, contains street-level photographs captured by three cameras fixed on the roof of car. A field survey was focused in the Vojvodina, Serbia to more closely examine land cover/land use within croplands monitored by LandSense citizen scientists (March-September 2018).</p> <p>The dataset has the following characteristics:</p> <ul> <li>Time period of data collection: Sep 2018</li> <li>Total number of photographs: 26759</li> <li>Region of interest: Vojvodina - Ruma municipality (Serbia)</li> </ul> <p>Associated files: Serbia Streetlevelimagery2018 – Attributes.txt, Serbia Streetlevelimagery2018.csv, Serbia Streetlevelimagery2018.zip</p> <p>This dataset is licensed under a Creative Commons Attribution 4.0 International. It is attributed to the <a href="https://landsense.eu/">LandSense Citizen Observatory</a>, <a href="https://ec.europa.eu/jrc/en">Joint Research Centre</a> and <a href="https://inosens.rs/">InoSens</a>.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 689812.</p>
Morpho-stratigraphic map of Jezero crater landing area
<p>The rover Perseverance of the Mars2020 mission will depart to Mars in July 2020 and land on Mars in February 2021. Its landing site, Jezero crater, has been selected due to the presence of two fan deltas, inlet and outlet valleys and a huge number of aqueous landforms (fluvial and lacustrine sediments).<br> This morpho-stratigraphic map has been produced from orbital visible imagery and its interpretations take into account the orbital facies (layers, massive, etc.) and their stratigraphic relationships, the texture and albedo of terrains, without taking into account mineralogical data. The area studied here is centered around the landing area comprising the east of the fan delta and the west of the crater floor. The map has been done at 1:10,000 scale.</p>
GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery
<p>A novel global 30-m land-cover product with a fine classification system for the year 2015 (GLC_FCS30-2015). The product was produced by combining time-series of Landsat imagery and high-quality training data from the GSPECLib (Global Spatial Temporal Spectra Library) on the Google Earth Engine computing platform. First, the global training data from the GSPECLib were developed by applying a series of rigorous filters to the MCD43A4 NBAR and CCI_LC land-cover products. Secondly, a local adaptive random forest model was built for each 5°×5° geographical tile by using the multi-temporal Landsat spectral and textures features of the corresponding training data, and the GLC_FCS30-2015 land-cover product containing 30 land-cover types was generated for each tile.</p>
A Dataset of European Union Land Cover Validation Samples
<p>A dataset of European Union land cover validation samples in 2015 and 2010 based on the LUCAS micro dataset ( publicly available at <a href="https://ec.europa.eu/eurostat/web/lucas/data/lucas-grid">https://ec.europa.eu/eurostat/web/lucas/data/lucas-grid</a> ) . The dataset provides 9 land cover types of land cover including cropland, forest, grassland, shrubland, wetland, water, bareland, impervious surface and permanent snow/ice. The dataset is provided in .csv format.</p>
Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2018: Globe
<p>Consolidated epoch 2018 from the Collection 3 of annual, global 100m land cover maps.</p> <p>Other available epochs: <a href="https://doi.org/10.5281/zenodo.3939038">2015</a> <a href="https://doi.org/10.5281/zenodo.3518026">2016</a> <a href="https://doi.org/10.5281/zenodo.3518036">2017</a> <a href="https://doi.org/10.5281/zenodo.3939050">2019</a></p> <p>Produced by the global component of the Copernicus Land Service, derived from PROBA-V satellite observations and ancillary datasets.</p> <p>The maps include </p> <ul> <li>a main discrete classification with 23 classes aligned with UN-FAO's Land Cover Classification System,</li> <li>a set of versatile cover fractions: percentage (%) of ground cover for the 10 main classes</li> <li>a forest type layer</li> <li>quality layers on input data density and on the confidence of the detected land cover change</li> </ul> <p><a href="https://land.copernicus.eu/global/lcviewer">Click here to view the maps</a></p> <p><a href="https://land.copernicus.eu/global/lcviewer">More information about the land cover maps</a></p> <p><a href="https://doi.org/10.5281/zenodo.3606295">Product User Manual</a></p>
Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2017: Globe
<p>Consolidated epoch 2017 from the Collection 3 of annual, global 100m land cover maps.</p> <p>Other available epochs: <a href="https://doi.org/10.5281/zenodo.3939038">2015</a> <a href="https://doi.org/10.5281/zenodo.3518026">2016</a> <a href="https://doi.org/10.5281/zenodo.3518038">2018</a> <a href="https://doi.org/10.5281/zenodo.3939050">2019</a></p> <p>Produced by the global component of the Copernicus Land Service, derived from PROBA-V satellite observations and ancillary datasets.</p> <p>The maps include</p> <ul> <li>a main discrete classification with 23 classes aligned with UN-FAO's Land Cover Classification System,</li> <li>a set of versatile cover fractions: percentage (%) of ground cover for the 10 main classes</li> <li>a forest type layer</li> <li>quality layers on input data density and on the confidence of the detected land cover change</li> </ul> <p><a href="https://land.copernicus.eu/global/lcviewer">Click here to view the maps</a></p> <p><a href="https://land.copernicus.eu/global/lcviewer">More information about the land cover maps</a></p> <p><a href="https://doi.org/10.5281/zenodo.3606295">Product User Manual</a></p>
Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe
<p>Near real time epoch 2019 from the Collection 3 of annual, global 100m land cover maps.</p> <p>Other available epochs: <a href="https://doi.org/10.5281/zenodo.3939038">2015</a> <a href="https://doi.org/10.5281/zenodo.3518026">2016</a> <a href="https://doi.org/10.5281/zenodo.3518036">2017</a> <a href="https://doi.org/10.5281/zenodo.3518038">2018</a></p> <p>Produced by the global component of the Copernicus Land Service, derived from PROBA-V satellite observations and ancillary datasets.</p> <p>The maps include</p> <ul> <li>a main discrete classification with 23 classes aligned with UN-FAO's Land Cover Classification System,</li> <li>a set of versatile cover fractions: percentage (%) of ground cover for the 10 main classes</li> <li>a forest type layer</li> <li>quality layers on input data density and on the confidence of the detected land cover change</li> </ul> <p><a href="https://land.copernicus.eu/global/lcviewer">Click here to view the maps</a></p> <p><a href="https://land.copernicus.eu/global/lcviewer">More information about the land cover maps</a></p> <p><a href="https://doi.org/10.5281/zenodo.3606295">Product User Manual</a></p>
Land cover, landscape metrics and typology of European cities for Urban Forest Ecosystem Services (UFES) evaluation
<p>The data refers to the paper "<em>Urban Forests as Regulating Ecosystems: Types and Ranking of European Cities</em>"</p> <p>The datasets provide a typology for 689 European urban areas, the land cover metrics and landscape metrics used to create the typology and the Urban Forest Ecosystem Services (UFES) indexes created from them.</p> <p>The typology of Urban Forest Ecosystem Services (UFES) presents 10 clusters of cities aggregated into 4 groups: Forest cities, Anthropogenic cities, Herbaceous cities and Standard European cities. The data can be used to support urban planning policies at local and regional scales; in urban forestry, urban form and ecosystem services work related at different spatial scales. The metrics used capture the spatial integration of different layers of natural, semi-natural and artificial land within functional urban areas.</p> <p> </p> <p>The datasets are a csv file (<code>Metrics.csv</code>) and a shapefile (<code>UFES.shp</code>) of polygons with attributes.</p> <ul> <li> <p><code>UFES.shp</code> attributes' are the following: FUA codes, country name, main city name, clusters and groups of FUAs resulting from the hierarchical cluster analysis (HCA), the R color codes used in the article, the five UFES budget indexes as well as an aggregated global UFES index for each FUA.</p> </li> <li> <p><code>Metrics.csv</code> contains the FUA codes, the land cover and landscape metrics used in the HCA.</p> </li> </ul> <p> </p>
ELC10: European 10 m resolution land cover map 2018
<p>Refer to preprint here: https://arxiv.org/abs/2104.10922</p> <p>A land cover classification for Europe at 10 m resolution produced with a machine learning workflow driven by Sentinel optical and radar satellite imagery. The classification model was trained on land cover reference data form the LUCAS (Land Use/Cover Area frame Survey) dataset. The map represents conditions in 2018.</p> <p>The methodology is currently under review, but this will be updated as soon as the paper is available online. Please refer to the publication for accuracy estimates and usage guidelines.</p> <p>The map is split up into a number of raster tiles with the coordinate reference system "EPSG:3035 - ERTS89 / LAEA Europe".The filename of each tile is in the form baseFilename-yMin-xMin where xMin and yMin are the coordinates of each tile within the overall bounding box of the entire ELC10 image.</p> <p>The pixel values, their definitions and suggested hex color codes include: 0 (not mapped #000000), 1 (Artificial land, #CC0303), 2 (Cropland, #CDB400), 3 (Woodland, #235123), 4 (Shrubland, #B76124), 5 (Grassland, #92AF1F), 6 (Bare land, #F7E174), 7 (Water/permanent snow/ice, #2019A4), 8 (Wetland, #AEC3D6).</p>
MASS2ANT Snowfall Dataset (Downscaling @5.5km over Dronning Maud Land, Antarctica, 1850 - 2014)
<p><strong>MASS2ANT Snowfall Dataset: Information for users</strong></p> <p>November 2020</p> <p> </p> <p>N. Ghilain<sup>1</sup>, S. Vannitsem<sup>1</sup>, Q. Dalaiden<sup>2</sup>, H. Goosse<sup>2</sup>, L. De Cruz<sup>1</sup></p> <p><sup>1</sup>Royal Meteorological Institute, Uccle, Belgium</p> <p><sup>2</sup>UCLouvain, Earth and Life Institute, Louvain-la-Neuve, Belgium</p> <p> </p> <p>contacts RMI: <a href="mailto:stephane.vannitsem@meteo.be">stephane.vannitsem@meteo.be</a>, <a href="mailto:nicolas.ghilain@meteo.be">nicolas.ghilain@meteo.be</a>, <a href="mailto:lesley.decruz@meteo.be">lesley.decruz@meteo.be</a></p> <p>contacts UCLouvain: <a href="mailto:hugues.goosse@uclouvain.be">hugues.goosse@uclouvain.be</a>, <a href="mailto:quentin.dalaiden@uclouvain.be">quentin.dalaiden@uclouvain.be</a></p> <p> </p> <p>We provide in this dataset maps at 5.5 km resolution of the daily and yearly accumulated snowfall over emerged land (Ice Sheet) of Dronning Maud Land (Antarctica) from 1850 to 2014. We used a statistical method to derive fine resolution maps from GCM runs (CESM2, 10 runs). In the method, we searched for analogs in a database we constructed from the association between re-analyses large-scale meteorological fields (ERA5 and ERA-Interim) and RCM daily accumulated snowfall (RACMO2.3p5.5). RACMO2.3p5.5 data are available freely on request (<a href="https://www.projects.science.uu.nl/iceclimate/models/antarctica.php">https://www.projects.science.uu.nl/iceclimate/models/antarctica.php</a>). CESM2 CMIP6 runs are also freely available (<a href="https://esgf-node.llnl.gov/search/cmip6/">https://esgf-node.llnl.gov/search/cmip6/</a>). The complete description of the algorithm and performance is described in: <em>Ghilain </em><em>N.,</em><em> Vannitsem </em><em>S.,</em><em> Dalaiden </em><em>Q.,</em><em> Goosse </em><em>H.,</em><em> De Cruz </em><em>L.,</em><em> </em><em>Wei</em><em> </em><em>W., </em><em>Reconstruction</em><em> of d</em><em>aily snowfall accumulation at 5.</em><em>5</em><em>km resolution over Dronning Maud Land, Antarctica, from 1850 to 2014 </em><em>using an analog-based downscaling technique</em>, submitted to Earth System Science Data (ESSD).</p> <p> </p> <p>The MASS2ANT Snowfall dataset is composed of the annual estimations of snowfall over Dronning Maud Land, the daily time series for the total period for all the emerged grid points of the domain (2 files are given in example, the all set is available on zenodo: 10.5281/zenodo.6355455, 10.5281/zenodo.6359385, 10.5281/zenodo.6362299), the principal components time series and Empirical Orthogonal Functions (EOF) offering the possibility to analyze the synoptic weather patterns associated to snowfall over the ice sheet and the Principal Component weights (PCs) time series from the re-analysis in case one wants to extend or improve the database. Realistic weather patterns can be recomposed in associating (product of matrices) the PCs with the EOFs</p> <p> </p> <p>The method could be easily expanded to other sources (other GCM, other reference RCM or other reanalysis), and potentially for other parts of Antarctica. Samples of the programs used to generate this database are therefore also provided here.</p> <p>Refer to <a href="https://zenodo.org/api/files/d8e358fa-f3d7-457e-949b-94f323ff04f9/MASS2ANT_Snowfall_Dataset_InfoUsers.pdf">MASS2ANT_Snowfall_Dataset_InfoUsers.pdf</a></p> <p> </p>
Agricultural land use and livestock composition by case study of the SURE-Farm project - Input data for a dynamic nitrogen flow model
<p>Dataset used as input to the model by Pinsard et al (2021) and results published in D5.5 of the SURE-Farm project.</p>
1988-2009 time-series of land-use/land-cover maps for the Mar Menor / Campo de Cartagena watershed by means of supervised classification of Landsat images.
<p>Serie de mapas de usos y coberturas de la cuenca del Mar Menor (SE España): 2009, 2000, 1997 y 1998. Así como el documento completo de tesis en las que se generaron y analizaron.</p> <p>Time-series of land-use / land-cover maps of Mar Menor watershed (SE Spain): 2009, 2000, 1997 y 1998. As well as the complete thesis document in which they were generated and analyzed.</p>
Social sensing of urban land use based on analysis of Twitter users' mobility patterns
<p>A companion dataset for the paper "Social sensing of urban land use based on analysis of Twitter users' mobility patterns". This dataset contains five files and one dictionary depicting the preferential return of Twitter users to their key locations and the urban land use types at these locations. More details can be found in the README file. </p>
AntarcticaLC2000: The new Antarctic land cover database for the year 2000
<p>Antarctic Land Cover Database for the Year 2000 (AntarcticaLC2000) was produced using Landsat Enhanced Thematic Mapper Plus (ETM+) data acquired around 2000 and Moderate Resolution Imaging Spectrometer (MODIS) images acquired in the austral summer of 2003/2004 according to the criteria for the 1:100,000-scale. Three land cover types were included in this map, separately, ice-free rocks, blue ice, and snow/firn. This classification legend was determined based on a review of the land cover systems in Antarctica (LCCSA) and an analysis of different land surface types and the potential of satellite data. Image classification was conducted through a combined usage of computer-aided and manual interpretation methods. Results show that the areas and percentages of ice-free rocks, blue ice, and snow/firn are 73,268.81 km<sup>2</sup> (0.537%), 225,937.26 km<sup>2</sup> (1.656%), and 13,345,460.41 km<sup>2</sup> (97.807%), respectively. The comparisons with other different data proved a higher accuracy of our product and a more advantageous data quality.</p>
LAMASUS Land Use Management Data Set
<p>The land use management classes developed as part of the LAMASUS project have been aggregated to a 1 km grid (INSPIRE) provided as three geopackages and NUTS2 regions, provided as shares in each grid cell and number of pixels in each region, respectively. The NUTS2 data can be joined to the NUTS2 shapefile (contained in NUTS0_2016_01M_3035_corrected.zip, including all NUTS levels), which is provided here as some corrections to the NUTS layer have been made. A list of the land use management classes is provided in the excel file. Note that these aggregated files are consistent with official statistics from the Forest Resources Assessment of the Food and Agriculture Organization (FRA-FAO) for forest areas and Eurostat for cropland and grassland areas. </p> <div> <p>This dataset has been created as part of LAMASUS Project under the scope of Deliverable 2.1 titled "The LUM Geodatabase and Area Estimates of Land Use Change to 2018 ". The data is directly linked to the work described on pages 12-34, belonging to section 3. The Land Use Management (LUM) Geodatabase. The full text of the deliverable can be accessed via: <a href="https://www.lamasus.eu/wp-content/uploads/LAMASUS_D2.1_LUMGeodatabase.pdf">https://www.lamasus.eu/wp-content/uploads/LAMASUS_D2.1_LUMGeodatabase.pdf.</a></p> </div>
The 30 m annual land cover datasets and its dynamics in China from 1985 to 2024
<p>Using 335,709 Landsat images on the Google Earth Engine, we built the first Landsat-derived annual land cover product of China (CLCD) from 1985 to 2019. We collected the training samples by combining stable samples extracted from China's Land-Use/Cover Datasets (CLUD), and visually-interpreted samples from satellite time-series data, Google Earth and Google Map. Several temporal metrics were constructed via all available Landsat data and fed to the random forest classifier to obtain classification results. A post-processing method incorporating spatial-temporal filtering and logical reasoning was further proposed to improve the spatial-temporal consistency of CLCD. </p> <p>"*_albert.tif" are projected files via a proj4 string "+proj=aea +lat_1=25 +lat_2=47 +lat_0=0 +lon_0=105 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs".</p> <p>CLCD in 2024 is now available.</p> <p>1. Given that the USGS no longer maintains the Landsat Collection 1 data, we are now using the <a href="https://www.usgs.gov/landsat-missions/landsat-collection-2">Collection 2</a> SR data to update the CLCD.</p> <p>2. All files in this version have been exported as Cloud Optimized GeoTIFF for more efficient processing on the cloud. Please check <a href="https://www.cogeo.org/">here</a> for more details.</p> <p>3. Internal overviews and color tables are built into each file to speed up software loading and rendering.</p>
HR land use maps part 2
<p>High resolution land use maps raster layers (.tif) with a spatial resolution of 10 m created by RethinkAcion project. These datasets are in ETRS89 in line with those that will be uploaded to the platform of the project. This v2 includes metadata</p>
Wet season standardised maximum land surface temperature of the Greater Paramaribo Region 2016-2018
<p>This map shows the maximum land surface temperature for the wet season of the Greater Paramaribo Region, Suriname, 2016-2018. The satellite images stem from the Landsat 8 OLI/TIRS (Operational Landsat Imager/Thermal Infrared Sensor) satellite and were obtained from the United States Geological Survey (USGS). A detailed description is provided in the metadata document.</p><p><i>Tom Remijn, Lisa Best, Rudi van Kanten, Nina Schwarz , Louise Willemen, 2020, Wet season standardised maximum land surface temperature of the Greater Paramaribo Region 2016-2018, product of 'Naar een groen en leefbaarder Paramaribo' by Tropenbos Suriname and University of Twente-ITC.</i> DOI: 10.5281/zenodo.7696837,<i> licensed under the </i><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/"><i>Creative Commons License CC BY-NC-SA 4.0</i></a><i>.</i></p>
Dry season standardised maximum land surface temperature of the Greater Paramaribo Region 2015-2019
<p>This map shows the maximum land surface temperature for the dry season of the Greater Paramaribo Region 2015-2019. The satellite images stem from the Landsat 8 OLI/TIRS (Operational Landsat Imager/Thermal Infrared Sensor) satellite. See details in the metadata document.</p><p>This map was made for the Tropenbos Suriname and the University of Twente-Faculty Geo-information Science and Earth Observation (ITC) project "Naar een groen en leefbaarder Paramaribo" and must be accredited as follows: </p><p><i>Tom Remijn, Lisa Best, Rudi van Kanten, Nina Schwarz , Louise Willemen, 2020, Dry season standardised maximum land surface temperature of the Greater Paramaribo Region 2015-2019, product of 'Naar een groen en leefbaarder Paramaribo' by Tropenbos Suriname and University of Twente-ITC.</i> DOI: 10.5281/zenodo.7696767,<i> licensed under the </i><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/"><i>Creative Commons License CC BY-NC-SA 4.0</i></a><i>.</i></p><p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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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.