Dataset Open Access
Stefanos Georganos; Tais Grippa
This is a very-high-resolution land cover map of Dar es Salaam derived from satellite imagery (Pleiades, 0.5m resolution). The majority of the area is classified from a 2016 (July) image while a small part of it from two images collected in January and March 2018, respectively.
The pixel values related to the following legend:
3=artificial ground surface
113=high elevated buildings
112=medium elevated buildings
111=low elevated buildings
The Out of Bag error of the product is 6,38% with the following class errors:
Building = 0.035826
Water = 0.049934
Artificial Ground Surface = 0.077108
Low Vegetation = 0.108709
Tall Vegetation = 0.062278
Bare Ground = 0.13803
Shadow = 0.019872
 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.
 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.
 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).