Published June 16, 2018 | Version V1.0
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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:

  • "" : Shapefile of the street blocks extracted from OpenStreetMap using [2] with classification results in the attribute table.
  • "" : 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


[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. 20187(7), 246. 

[2] Grippa, Tais. 2018. “Osm Street Blocks Extraction.” Zenodo.


This dataset was produced in the frame of two research project : MAUPP ( and REACT (, funded by the Belgian Federal Science Policy Office (BELSPO).


The production of this dataset was founded by BELSPO (Belgian Federal Science Policy Office) in the frame of the STEREO III program, as part of the MAUPP (SR/00/304) and REACT (SR/00/337) project ( and


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