June 16, 2018 Dataset Open Access

Dakar land use map at street block level

Tais Grippa; Stefanos Georganos

Other(s)

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).