Dar Es Salaam Very-High-Resolution Land Cover Map

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: 5=tree<br> 8=shadow<br> 3=artificial ground surface<br> 4=low vegetation<br> 2=water<br> 7=bare ground<br> 1=building<br> 113=high elevated buildings<br> 112=medium elevated buildings<br> 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 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.” <em>Remote Sensing</em> 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 <em>Proceedings Volume 10431, Remote Sensing Technologies and Applications in Urban Environments II.</em>, 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 <em>Proceedings of the 2017 Conference on Big Data from Space (BiDS’17)</em>. This dataset was produced in the frame of REACT (http://react.ulb.be), funded by the Belgian Federal Science Policy Office (BELSPO).