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13,117 results for “Maps”
Dakar very-high resolution land cover map
<p>This land cover map of Dakar (Senegal) was created from a Pléiades very-high resolution imagery with a spatial resolution of 0.5 meter. The methodology followed a open-source semi-automated framework [1] that rely on <a href="https://grass.osgeo.org/">GRASS GIS</a> using a local unsupervised optimization approach for the segmentation part [2-3].</p> <p>Description of the files:</p> <ul> <li>"Landcover.zip" : The direct output from the supervised classification using the Random Forest classifier.</li> <li>"Landcover_Postclassif_Level8_Splitbuildings.zip" : Post-processed version of the previous map ("Landcover"), with reduced misclassifications from the original classification (rule-based used to reclassify the errors, with a focus on built-up classes).</li> <li>"Landcover_Postclassif_Level8_modalfilter3.zip" : Smoothed version of the previous product (modal filter with window 3x3 applied on the "Landcover_Postclassif_Level8_Splitbuildings"). </li> <li>"Landcover_Postclassif_Level9_Shadowsback.zip" : Corresponds to the "level8_Splitbuildings" with shadows coming from the original classification.</li> <li>"Dakar_legend_colors.txt" : Text file providing the correspondance between the value of the pixels and the legend labels and a proposition of color to be used.</li> </ul> <p> </p> <p>References:</p> <p>[1] Grippa, Taïs, Moritz Lennert, Benjamin Beaumont, Sabine Vanhuysse, Nathalie Stephenne, and Eléonore Wolff. 2017. “An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification.” <em>Remote Sensing</em> 9 (4): 358. <a href="https://doi.org/10.3390/rs9040358">https://doi.org/10.3390/rs9040358</a>.</p> <p>[2] Grippa, Tais, Stefanos Georganos, Sabine G. Vanhuysse, Moritz Lennert, and Eléonore Wolff. 2017. “A Local Segmentation Parameter Optimization Approach for Mapping Heterogeneous Urban Environments Using VHR Imagery.” In <em>Proceedings Volume 10431, Remote Sensing Technologies and Applications in Urban Environments II.</em>, edited by Wieke Heldens, Nektarios Chrysoulakis, Thilo Erbertseder, and Ying Zhang, 20. SPIE. <a href="https://doi.org/10.1117/12.2278422">https://doi.org/10.1117/12.2278422</a>.</p> <p>[3] Georganos, Stefanos, Taïs Grippa, Moritz Lennert, Sabine Vanhuysse, and Eleonore Wolff. 2017. “SPUSPO: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Efficiently Segmenting Large Heterogeneous Areas.” In <em>Proceedings of the 2017 Conference on Big Data from Space (BiDS’17)</em>.</p> <p> </p> <p>Founding: </p> <p>This dataset was produced in the frame of two research project : MAUPP (<a href="http://maupp.ulb.ac.be">http://maupp.ulb.ac.be</a>) and REACT (<a href="http://react.ulb.be">http://react.ulb.be</a>), funded by the Belgian Federal Science Policy Office (<a href="http://eo.belspo.be/About/Stereo3.aspx">BELSPO</a>).</p>
Ouagadougou very-high resolution land cover map
<p>This land cover map of Ouagadougou (Burkina Faso) was created from a WorldView3 very-high resolution imagery with a spatial resolution of 0.5 meter. The methodology followed a open-source semi-automated framework [1] that rely on <a href="https://grass.osgeo.org/">GRASS GIS</a> using a local unsupervised optimization approach for the segmentation part [2-3].</p> <p>Description of the files:</p> <ul> <li>"Landcover.zip" : The direct output from the supervised classification using the Random Forest classifier.</li> <li>"Landcover_Postclassif_Level5_Splitbuildings.zip" : Post-processed version of the previous map ("Landcover"), with reduced misclassifications from the original classification (rule-based used to reclassify the errors, with a focus on built-up classes).</li> <li>"Landcover_Postclassif_Level5_modalfilter3.zip" : Smoothed version of the previous product (modal filter with window 3x3 applied on the "Landcover_Postclassif_Level5_Splitbuildings"). </li> <li>"Landcover_Postclassif_Level6_Shadowsback.zip" : Corresponds to the "level5_Splitbuildings" with shadows coming from the original classification.</li> <li>"Ouaga_legend_colors.txt" : Text file providing the correspondance between the value of the pixels and the legend labels and a proposition of color to be used.</li> </ul> <p> </p> <p>References:</p> <p>[1] Grippa, Taïs, Moritz Lennert, Benjamin Beaumont, Sabine Vanhuysse, Nathalie Stephenne, and Eléonore Wolff. 2017. “An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification.” <em>Remote Sensing</em> 9 (4): 358. <a href="https://doi.org/10.3390/rs9040358">https://doi.org/10.3390/rs9040358</a>.</p> <p>[2] Grippa, Tais, Stefanos Georganos, Sabine G. Vanhuysse, Moritz Lennert, and Eléonore Wolff. 2017. “A Local Segmentation Parameter Optimization Approach for Mapping Heterogeneous Urban Environments Using VHR Imagery.” In <em>Proceedings Volume 10431, Remote Sensing Technologies and Applications in Urban Environments II.</em>, edited by Wieke Heldens, Nektarios Chrysoulakis, Thilo Erbertseder, and Ying Zhang, 20. SPIE. <a href="https://doi.org/10.1117/12.2278422">https://doi.org/10.1117/12.2278422</a>.</p> <p>[3] Georganos, Stefanos, Taïs Grippa, Moritz Lennert, Sabine Vanhuysse, and Eleonore Wolff. 2017. “SPUSPO: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Efficiently Segmenting Large Heterogeneous Areas.” In <em>Proceedings of the 2017 Conference on Big Data from Space (BiDS’17)</em>.</p> <p> </p> <p>Founding: </p> <p>This dataset was produced in the frame of two research project : MAUPP (<a href="http://maupp.ulb.ac.be">http://maupp.ulb.ac.be</a>) and REACT (<a href="http://react.ulb.be">http://react.ulb.be</a>), funded by the Belgian Federal Science Policy Office (<a href="http://eo.belspo.be/About/Stereo3.aspx">BELSPO</a>).</p>
Maps of subjective feelings
<p>This is a snapshot of the code + data for the project "Map of subjective feelings".</p> <p>Up-to-date version of code and documentation at:</p> <p><a href="https://version.aalto.fi/gitlab/eglerean/sensations">https://version.aalto.fi/gitlab/eglerean/sensations</a></p> <p>Note: although the filesize is not big, the project contains ~43 thousand files; please consider that before unzipping.</p>
CLAMATO2017: IGM Lyman-alpha Forest Tomography Survey Public Data Release of Spectra and Maps
<p><strong>CLAMATO 2017 Data Release 1 </strong></p> <p>Public release: 2017 October 9</p> <p>Uploaded to Zenodo on 2018 June 19th after acceptance for publication in ApJS</p> <p>By Khee-Gan Lee (kglee@lbl.gov) and collaborators</p> <p>Supporting paper: https://arxiv.org/abs/1710.02894</p> <p>These are data products associated with the first data release of the COSMOS Lyman-Alpha Mapping And Tomography Observations (CLAMATO) survey with the Keck-I telescope, which mapped 3D Lyman-alpha forest absorption at 2.05<z<2.55 within the COSMOS field.</p> <p>The following is the summary of the main products:<br> - Source catalog (CL2017_VALUEADDED_RELEASE_20171009.TXT)<br> - Reduced spectra, in /spec_v0/ (blue) and /spec_v0_red (red) sub-directories<br> - Continuum-fitted 2.05<z<2.55 Lyman-alpha forest pixel data (pixel_data.bin)<br> - Wiener-reconstructed 3D absorption map (map_2017_v3.bin)</p> <p>Versions:<br> v0 (not public): Initial rough extraction for 2.15<z<2.55 <br> v1 (not public): Extended redshift range to 2.05<z<2.55 <br> v2 (not public): Caught bug that caused wrong [RA,Dec] for ~4-5 objects<br> v3 (released 2017 Oct 9): Fixed bug that caused wrong aspect ratio in output map<br> v4 (released 2018 Mar 29): Fixed bug that caused negative continua in some spectra</p> <p><br> <strong>Redshift Catalog and Spectra</strong> </p> <p>We provide our redshift catalog and reduced spectra obtained with Keck-I/LRIS</p> <p>The source catalog is provided in the ASCII file CL2017_VALUEADDED_RELEASE_20171009.TXT, with the following columns:</p> <p>- BLUE_SPEC: Blue spectrum filename (in /spec_v0/ sub-directory)<br> - TOMO_ID: CLAMATO ID number<br> - GMAG: g-magnitude (AB) per Capak et al 2007 photometric catalog<br> - CONF: Redshift confidence grade: see https://arxiv.org/abs/1710.02894<br> - ZSPEC: Spectroscopic redshift as determined from CLAMATO spectrum<br> - QSO: QSO flag (1 if QSO, 0 if non-QSO)<br> - RA: R.A. in degrees (J2000)<br> - DEC: Dec in degrees (J2000)<br> - S/N_1: Estimated Lya-forest S/N at 2.05<z<2.15, -9.0 denotes no estimate<br> - S/N_2: Estimated Lya-forest S/N at 2.15<z<2.35, -9.0 denotes no estimate<br> - S/N_2: Estimated Lya-forest S/N at 2.35<z<2.55, -9.0 denotes no estimate<br> - S/N_RED: Estimated S/N over restframe 1250 ang < lambda < 1350 ang, -9.0 denotes no estimate<br> - TOMOFLAG: Flag on whether sightline was used in tomographic map (0 for no, 1 for yes)<br> - EXPTIME: Exposure time on the spectrum, in seconds (aggregate)<br> - RED_SPEC: Red spectrum filename (in /spec_v0_red/ sub-directory), 'NA' if doesn't exist</p> <p>The tarballs spec_v0.tar.gz and spec_v0_red.tar.gz include all the reduced spectra from LRIS-Blue and LRIS-Red, respectively.</p> <p>The individual LRIS spectra are provided in FITS format, with the following HDU Extensions:<br> - HDU0: Object spectral flux density, in units of 10^{-17} ergs/s/cm^2/angstrom<br> - HDU1: Noise standard deviation<br> - HDU2: Pixel Wavelengths in angstroms</p> <p><strong>Pixel Data </strong></p> <p>The binary file PIXEL_DATA_v4.BIN stores the concatenated Lyman-alpha forest pixels at 2.05<z<2.55 that have been extracted from the 1D spectra and continuum-fitted. </p> <p>The first value in the binary is a 32-bit integer specifying the number of pixels (64332), followed by 5 double-precision floating point (64-bit) vectors storing the x, y, z, sigma_f, and delta_f of the pixels.</p> <p>An example python script to read pixel_data is as follows:<br> import numpy as np<br> with open('CLAMATO2017_public/pixel_data_v4.bin','r') as f:<br> npix = np.fromfile(f, dtype=np.int32, count=1)<br> f.seek(4)<br> pixel_data = np.fromfile(f,dtype=np.float64).reshape((npix,5))</p> <p>LIST_TOMO_INPUT_2017.TXT is a summary file of corresponding to PIXEL_DATA.BIN, listing the [x,y,z] position of the sightlines that contributed to the file as well as, in the final two columns, the index range that can be used to grab the relevant pixels from the concatenated pixel list.</p> <p><strong>Tomographic Map</strong></p> <p>The Wiener-reconstructed map of the 2.05<z<2.55 IGM within the CLAMATO field is the result of applying the dachshund algorithm (http://github.com/caseywstark/dachshund) to PIXEL_DATA.BIN, with the configuration file INPUT.CFG . (Caveat: the version of PIXEL_DATA.BIN here is not actually the right version to directly input into the dachshund code: the first integer in this file should not be present for input to dachshund). </p> <p>The reconstructed map is MAP_2017_V4.BIN, which is a 60x48x876 = 2552880 pixel double-precision binary with. The dimension that changes fastest is the z-dimension (876 pixels per dimension), followed by the y-dimension (48 pixels per dimension) and x-dimension (60 per dimension).</p> <p>Each map pixel represents a 0.5Mpc/h comoving voxel of the Ly-alpha forest absorption. See the Appendix of https://arxiv.org/abs/1710.02894 for the conversion factors to assume to switch between pixel/voxel and [RA, Dec, redshift].</p> <p>The file MAP_2017_V4_SM2.0.BIN is the same map, but smoothed with a R=2Mpc/h Gaussian kernel.</p>
SPICES – Sea ice edge maps from the Fram Strait
<p>Sea ice edge maps derived from Sentinel-1 SAR dual polarisation EW images using a Support Vector Machine (SVM) algorithm. Thisalgorithm is based on a SVM approach, and in addition uses texture calculation and principal component analysis (PCA) to classify sea ice types (Korosov et al., 2016). The main steps of the algorithms include:<br> (1) pre-processing of the raw SAR data, <br> (2) calculation of texture features, <br> (3) unsupervised pre-classification of the image using PCA and k-means cluster analysis to reduce the number of ice classes, <br> (4) expert re-classification of the image into the pre-calculated classes, <br> (5) training of the SVM using input from the previous step, and <br> (6) classifying the full image into the reduced number of classes using the trained SVM. <br> To generate an ice edge product, the SVM algorithm is used with only two classes: sea ice and open water. </p> <p>Korosov, A., N. Zakhvatkina, A. Vesman, A. Mushta, and S. Muckenhuber, Sea ice classification algorithm for Sentinel-1 images, Poster at ESA Living Planet Symposium 2016, Prague, Czech Republic, 9-13 may, 2016.</p> <p> </p> <p> </p>
3D printed map for blind or visually impaired people
<p>This data set is composed of three parts each having its proper origins, formats and rights. This data set was used to apply the methods of relief editing and image processing to facilitate the production of accessible documentation by having in hand an easy to use interface.</p>
SPICES – Sea ice type maps from Fram Strait, Barents and Kara Sea
<p>Sea ice type classification of Sentinel-1 SAR dual polarization EW images. </p> <p>Prior to classification, Sentinel-1 SAR images have been pre-processed to remove noise [1]. The procedure of classification is comprised of the following steps: 1. Haralick texture features are computed, 2. Principal Component Analysis (PCA) is applied, 3. K-means clustering is used to group the data into 15 clusters using factor scores of the PCA as input, 4. The 15 clusters are analysed by an ice expert and classified as open water and different types of sea ice.</p> <p> </p> <p>[1] J.-W. Park, A. A. Korosov, M. Babiker, S. Sandven, J.-S. Won, Efficient Thermal Noise Removal for Sentinel-1 TOPSAR Cross-Polarization Channel, IEEE Transactions on Geoscience and Remote Sensing, 2018; 56, 3, DOI:10.1109/TGRS.2017.2765248.</p>
SPICES – Sea ice edge maps from the Kara Sea
<p>Sea ice edge maps derived from Sentinel-1 SAR dual polarisation EW images using a Support Vector Machine (SVM) algorithm. This algorithm is based on a SVM approach, and in addition uses texture calculation and principal component analysis (PCA) to classify sea ice types (Korosov et al., 2016). The main steps of the algorithms include: (1) pre-processing of the raw SAR data, (2) calculation of texture features, (3) unsupervised pre-classification of the image using PCA and k-means cluster analysis to reduce the number of ice classes, (4) expert re-classification of the image into the pre-calculated classes, (5) training of the SVM using input from the previous step, and (6) classifying the full image into the reduced number of classes using the trained SVM. To generate an ice edge product, the SVM algorithm is used with only two classes: sea ice and open water. </p> <p>Korosov, A., N. Zakhvatkina, A. Vesman, A. Mushta, and S. Muckenhuber, Sea ice classification algorithm for Sentinel-1 images, Poster at ESA Living Planet Symposium 2016, Prague, Czech Republic, 9-13 may, 2016.</p>
Map. Historic map of the Vākāṭaka realm and neighbouring kingdoms
<p>Map. Historic map of the Vākāṭaka realm and neighbouring kingdoms and principalities of the Viṣṇukuṇḍins, Valkhās, Traikūṭakas, Nalas, Rāṣṭrakūṭas, Cāḷukyas, Sanakānikas, Śarabhapurīyas, Sālaṅkāyaṇas, Pitṛbhaktas and Uccakalpas.</p>
Non-destructive thickness mapping of wafer-scale hexagonal boron nitride down to a monolayer
<p>Raw data associated with the publication "Non-Destructive Thickness Mapping of Wafer-Scale Hexagonal Boron Nitride<br> Down to a Monolayer" published in ACS Applied Materials & Interfaces under the DOI: 10.1021/acsami.8b08609</p>
Fuzzy modelling and mapping soil moisture in Germany, link to research data and scientific software
<p>Research data and scientific software related to spatio-temporal estimations of ecological soil moisture with available data covering the whole territory of Germany and the Kellerwald National Park (Hesse). Temporal trends of modelled soil moisture for the time period 1961–2070 were statistically analyzed. Soil moisture changes (drying-out) at both national and regional levels were mapped.</p>
Map of the Manding language continuum
<p>Map of the Manding language continuum with approximate zones of major spoken varieties (Mandinka, Maninka, Bamanan and Jula) and shading of general areas where Manding functions as a lingua franca.</p> <p>Adapted from data of the following:</p> <p>Vydrin, Valentin, T.G. Bergman, and Matthew Benjamin. 2001. “Mandé Language Family of West Africa: Location and Genetic Classification.” <em>SIL International</em> 2000 (003). SIL Electronic Survey Reports (April 1). <a href="http://www.sil.org/silesr/2000/2000-003/Manding/Manding.htm">http://www.sil.org/silesr/2000/2000-003/Manding/Manding.htm</a>.</p>
Map of 1992-2015 landscape change trajectories
<p>Map of 1992-2015 landscape change trajectories. Landscapes are colored depending on their change trajectories and a percentage of changed area; small < 10%, medium (10% to 30%), and large (> 30%).</p> <p>To access and visualize the map use: <a href="https://landgis.opengeohub.org/#/?base=OpenTopoMap&opacity=80&layer=ldg_landscape.degradation_sil.9km_c"><strong>https://landgis.opengeohub.org/#/?base=OpenTopoMap&opacity=80&layer=ldg_landscape.degradation_sil.9km_c</strong></a></p> <p>Creation of this map is explained in details at <a href="https://www.sciencedirect.com/science/article/pii/S0303243418305841">https://www.sciencedirect.com/science/article/pii/S0303243418305841</a> (preprint at <a href="https://eartharxiv.org/k3rmn/">https://eartharxiv.org/k3rmn/</a>).</p>
Heat load maps at 100m resolution (Linz)
<p>Climate indices (e.g. mean annual number of summer days, hot days, tropical nights) for 30-year historical/future climate periods. The calculation method is based on the cuboid method, a statistical-dynamical downscaling procedure that combines high-resolution (100m) urban climate simulations with long-term climate information from monitoring data/regional climate projections.</p> <p><strong>Climate indices for historical/current periods:</strong> - Background climate information: monitoring data from the airport station Linz Hoersching (1961-2010) - Background climate information: historical (bias-corrected) EURO-CORDEX simulations (1971-2000)</p> <p><strong>Climate indices for future periods:</strong> - Background climate information: bias-corrected EURO-CORDEX model simulations for different representative concentration pathways (2021-2100)</p>
PARTHENOS Mappings
<p>This dataset contains the mappings to transform metadata available from the different research infrastructures involved in the PARTHENOS project from their input format to the Parthenos Entities Model (http://parthenos.d4science.org/CRMext/CRMpe.rdfs)<br> Mappings are 3M Mappings defined by domain and data experts using the 3M Editor (https://github.com/isl/3MEditor) of the X3ML Toolkit.</p> <p>Mappings are executed by the PARTHENOS Aggregator, a customisation of the D-NET Software Toolkit. A basic version of the aggregator is available on github at https://github.com/dnet-team/dnet-basic-aggregator/tree/dnet50-basic-aggregator.</p>
Topic Map for KPI analysis of e-Infrastructure projects
<p>This is a Topic Map from the e-IRG Knowledge Base used by the e-IRGSP5 project to analyse Key Performance Indicators (KPIs) for e-Infrastructure projects funded by Horizon 2020.</p>
Malwa site survey : archaeological site distribution maps
<p>Malwa site survey : archaeological site distribution maps. (1) mosaic based on Survey of India maps; (2) (3) study area; (4) Vidisha Raisen area.</p>
Kinematically collected reference fingerprint map (RFM) with the high precision tracking system for feature-based indoor positioning
<p>The offline referencing phase, one of the core phases of the fingerprinting-based indoor positioning system (FIPS), is the key stage for deploying the positioning system. The reference fingerprint map (RFM) is acquired for representing the relationship between location-relevant features and the corresponding locations and used for inferring the user’s location at the online stage. The kinematically collecting the RFM using the mobile device with the help of high precision tracking system is contributed to the community for benchmarking comparison of the indoor positioning performance. The detailed description of the data is cooming soon.<br> </p>
Carbon x-ray Raman scattering mapping and spectroscopy of a fragment of Lepidodendron trunk from the Upper Carboniferous
<p>Carbon x-ray Raman scattering mapping and spectroscopy of a fragment of Lepidodendron trunk from the Upper Carboniferous (ca. 305 Mya) of Noyelles-lez-Lens, France</p>
XRS carbon K-edge speciation mapping of an Eocene (ca. 53 Mya) ant entrapped in amber from Oise, France
<p>XRS carbon K-edge speciation mapping of an Eocene (ca. 53 Mya) ant entrapped in amber from Oise, France</p>
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