Dataset Open Access

Dakar very-high resolution land cover map

Tais Grippa; Stefanos Georganos

Other(s)
Yann Forget; Sabine Vanhuysse; Éléonore Wolff; Catherine Linard; Marius Gilbert; Michal Shimoni; Andrew Tatem; Nicole Van Lipzig; Marco millones; Benoit Parmentier; Brian McGill; Oscar Brousse; Matthias Demuzere; Daniel C Casey

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 GRASS GIS using a local unsupervised optimization approach for the segmentation part [2-3].

Description of the files:

  • "Landcover.zip" : The direct output from the supervised classification using the Random Forest classifier.
  • "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).
  • "Landcover_Postclassif_Level8_modalfilter3.zip" : Smoothed version of the previous product (modal filter with window 3x3 applied on the "Landcover_Postclassif_Level8_Splitbuildings"). 
  • "Landcover_Postclassif_Level9_Shadowsback.zip" : Corresponds to the "level8_Splitbuildings" with shadows coming from the original classification.
  • "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.

 

References:

[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.” Remote Sensing 9 (4): 358. https://doi.org/10.3390/rs9040358.

[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 Proceedings Volume 10431, Remote Sensing Technologies and Applications in Urban Environments II., edited by Wieke Heldens, Nektarios Chrysoulakis, Thilo Erbertseder, and Ying Zhang, 20. SPIE. https://doi.org/10.1117/12.2278422.

[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 Proceedings of the 2017 Conference on Big Data from Space (BiDS’17).

 

Founding: 

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).

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 (http://maupp.ulb.ac.be and http://react.ulb.be/).
Files (486.7 MB)
Name Size
Dakar_Landcover.zip
md5:71aa1d3d2ff67aee8ae6995db18fa50d
127.2 MB Download
Dakar_Landcover_Postclassif_Level8_modalfilter3.zip
md5:bbb843bbc48f5498d0fe613a4a4cc12f
108.5 MB Download
Dakar_Landcover_Postclassif_Level8_Splitbuildings.zip
md5:3cde4a568e4fe5de2cd2a25e0a2792d2
116.8 MB Download
Dakar_Landcover_Postclassif_Level9_Shadowsback.zip
md5:3dcb6af8804d1774c4ff0af0ae212ce1
134.2 MB Download
Dakar_legend_colors.txt
md5:91fe0574c87734b521988c247e387cea
361 Bytes Download
320
74
views
downloads
All versions This version
Views 320320
Downloads 7474
Data volume 7.1 GB7.1 GB
Unique views 308308
Unique downloads 3535

Share

Cite as