Journal article Open Access

Kampala Very-High-Resolution Land Cover Map

Stefanos Georganos; Tais Grippa

This is a very-high-resolution map of Kampala derived from satellite imagery of Pleiades (0.5m) collected in February 2013.

The pixel values related to the following legend:

2: Water

3: Tree vegetation

4:Low vegetation

5:Bare ground

6: Artificial ground surface

7:Buildin

8:Shadow

The Out of Bag error of the product is 14,14%. The class errors are:

Water = 0.077748

Tall Vegetation = 0.410714

Low Vegetation = 0.087757

Bare Ground = 0.336689

Artificial Ground Surface = 0.197932

Building = 0.058271

Shadow = 0.062032

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).

This research was funded by BELSPO (Belgian Federal Science Policy Office) in the frame of the STEREO III program, as part of the REACT (SR/00/337) project (http://react.ulb.be/).

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