Dakar land use map at street block level

This datatset contains a land use classification of Dakar (Senegal) at the street block level. It was created following the methodology presented in [1]. Description of the files: "Dakar_landuse_shapefile.zip" : Shapefile of the street blocks extracted from OpenStreetMap using [2] with classification results in the attribute table. "Dakar_landuse_style.zip" : Files for style of the shapefile. Attribute table content: "CAT", "GID" : ID of the street block "PROB_ACS" : Probability to belong to class ACS "PROB_AGRI" : Probability to belong to class AGRI "PROB_BARE" : Probability to belong to class BARE "PROB_DEPR" : Probability to belong to class DEPR "PROB_PLAN" : Probability to belong to class PLAN "PROB_VEG" : Probability to belong to class VEG "FIRST_LABE" : Class with the highest classification probability "SEC_LABEL" : Class with the second highest classification probability "FIRST_PROB" : Value of the highest classification probability "SEC_PROB" : Value of the second highest classification probability "UNCERTAIN" : Difference between "FIRST_PROB" and "SEC_PROB" "BUILT_PERC" : Percentage of the street blocks covered by built-up (from land cover map) "MAP_LABEL" : Final classification label with uncertainty and different density classes Legend classes label: "AGRI" : Agricultural vegetation "VEG" : Natural vegetation "BARE" : Bare soils "ACS" : Non-residential built-up (administrative, commercial, services, etc.) "PLAN" : Planned residential built-up "PLAN_LD" : Planned residential low density built-up "DEPR" : Deprived residential built-up "UNCERT" : Uncertain classification References: [1] Grippa, Tais, 2018, "Mapping urban land use at street block level using OpenStreetMap, remote sensing data and spatial metrics", ISPRS Int. J. Geo-Inf. 2018, 7(7), 246. https://doi.org/10.3390/ijgi7070246 [2] Grippa, Tais. 2018. “Osm Street Blocks Extraction.” Zenodo. https://doi.org/10.5281/zenodo.1290637. Funding: This dataset was produced in the frame of two research project : MAUPP (http://maupp.ulb.ac.be) and REACT (http://react.ulb.be), funded by the Belgian Federal Science Policy Office (BELSPO).