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5 results for “Ouagadougou”
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>
Ouagadougou land use map at street block level
<p>This datatset contains a land use classification of Ouagadougou (Burkina Faso) at the street block level. It was created following the methodology presented in [1].</p> <p>Description of the files:</p> <ul> <li>"Ouagadougou_landuse.gpkg" : GeoPackage with two layers: (1) layer of the street blocks extracted from OpenStreetMap using [2] with classification results in the attribute table. (2) layer with manual correction made by GEORGANOS Stefanos (sgeorganos@ulb.ac.be).</li> <li>"Ouagadougou_landuse_style_QGIS.zip" : Files for style for rendering in QGIS.</li> </ul> <p>Attribute table content:</p> <ul> <li>"CAT", "GID" : ID of the street block</li> <li>"PROB_ACS" : Probability to belong to class ACS</li> <li>"PROB_BARE" : Probability to belong to class BARE</li> <li>"PROB_PLAN" : Probability to belong to class PLAN</li> <li>"PROB_UNPLA" : Probability to belong to class UNPLAN</li> <li>"PROB_VEG" : Probability to belong to class VEG</li> <li>"FIRST_LABE" : Class with the highest classification probability</li> <li>"SEC_LABEL" : Class with the second highest classification probability</li> <li>"FIRST_PROB" : Value of the highest classification probability</li> <li>"SEC_PROB" : Value of the second highest classification probability</li> <li>"UNCERTAIN" : Difference between "FIRST_PROB" and "SEC_PROB"</li> <li>"BUILT_PERC" : Percentage of the street blocks covered by built-up (from land cover map)</li> <li>"MAP_LABEL" : Final classification label with uncertainty and different density classes. Depending on the layer, the label is with or without manual corrections</li> </ul> <p>Legend classes label:</p> <ul> <li>"VEG" : Vegetation</li> <li>"BARE" : Bare soils</li> <li>"ACS" : Non-residential built-up (administrative, commercial, services, etc.)</li> <li>"PLAN" : Planned residential built-up</li> <li>"PLAN_LD" : Planned residential low density built-up</li> <li>"UNPLAN" : Unplanned residential built-up</li> <li>"UNPLAN_LD" : Unplanned residential low density built-up</li> <li>"UNCERT" : Uncertain classification</li> <li>"WET" : Wetlands</li> <li>"AGRI" : Agricultural land</li> </ul> <p>References:</p> <p>[1] Grippa, Tais, 2018, "Mapping urban land use at street block level using OpenStreetMap, remote sensing data and spatial metrics", <em>ISPRS Int. J. Geo-Inf.</em> <strong>2018</strong>, <em>7</em>(7), 246. <a href="https://doi.org/10.3390/ijgi7070246">https://doi.org/10.3390/ijgi7070246</a> </p> <p>[2] Grippa, Tais. 2018. “Osm Street Blocks Extraction.” Zenodo. <a href="https://doi.org/10.5281/zenodo.1290637">https://doi.org/10.5281/zenodo.1290637</a>.</p> <p>Funding: </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>
Rainfall maps from commercial microwave link data for Ouagadougou, Burkina Faso
<p>Rainfall maps from commercial microwave link data for Ouagadougou, Burkina Faso, accompanying a submission to <em>Journal of Hydrometeorology </em>(<em>JHM</em>) are store in <em>CML_rainfall_maps_Ougadougou.nc</em> . The generation of these rainfall maps is described in the manuscript.</p> <p>The data are stored in a netCDF file with the dimensions <em>x</em>, <em>y</em> and <em>time</em>, <em>longitude</em> and <em>latitude</em> values defining the grid, and the variable <em>rain_rate </em>holding the rainfall maps.</p> <ul> <li>The dataset covers the area over Ouagadougou (longitude: -1.61 to -1.415 and latitude: 12.27 to 12.45) in a regular space grid of 72x79 with 0.55 km grid size.</li> <li>The temporal resolution is 5 minutes for the period from 2020-06-29 to 2020-10-10 <em> </em></li> </ul> <p>As supplementary material an animation (<em>Animation_2020_08_21.mp4</em>) showcases two rainfall events from 18:30 to 21:00 UTC on 2020-08-21.</p>
Typhoid Conjugate Vaccine Trial Among Children Younger Than 2 Years in Ouagadougou, Burkina Faso
ClinicalTrials.gov study NCT03614533. IPD Sharing: NO. Countries: 1. Publications: 1.
Assessing the Effect of Cool Roofs on Health Using Smartwatches in Ouagadougou, Burkina Faso
ClinicalTrials.gov study NCT06842641. IPD Sharing: YES. Countries: 1. Publications: 0.
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