Mapping urban areas with remote sensing is particularly challenging due to the spatial spectral heterogeneity of human settlements and their complex dynamics related to land cover change. Sentinel-2 offers improved spatial, temporal and spectral resolution compared to similar globally operating optical systems and is, therefore, a highly interesting data source for urban monitoring. In this regard, efficient workflows are desired to make best use of Sentinel’s rapidly growing archive. Moreover, the contribution of Sentinel-2, as a globally available source, to the existing state-of-the-art in urban mapping must be better understood. Especially its complementarity to existing urban mapping products is of interest. In this study, we used machine learning regression to produce an impervious surface fraction layer for Berlin, Germany, and surrounding areas from Sentinel-2 data. For model training, we used synthetic mixtures generated from an image spectral library representing pure surface cover types. We apply support vector regression (SVR) in order to efficiently and accurately produce surface cover fractions. Based on these fractions we performed a systematic quality assessment of our product in comparison to existing continental and global scale settlement maps, such as - the decameter resolution Global Urban Footprint (GUF) based on TerraSAR-X data, - the Global Human Settlement Layer (GHS) based on Landsat imagery and other open source data, - the European Settlement Map (ESM) with a focus on built-up vs non-built-up areas based on SPOT imagery and the EEA Urban Atlas, - and the Copernicus Imperviousness (CopImp) product. Our library-based approach is independent from the tedious spatial definition of training areas and appears promising for the generalized mapping of built-up areas across multiple cities. For validation, we defined 13 regions of interest in neighbourhoods with different characteristics and performed a pixel-wise comparison with cadastral reference data. The accuracy of the SVR product is largely similar to that of the other evaluated datasets with regard to above-ground human infrastructure. As also reported in Klotz et al. (Rem. Sens. Of Env., 178, 2016), we find that GUF and GHS are particularly useful to map dense settlements, but systematically overestimate urban coverage in low-density built-up areas. Whereas accuracies for dense urban areas are similar among the datasets, GUF and GHS feature lower user’s and producer’s accuracies than SVR and ESM in some neighbourhoods. SVR is comparable to CopImp in terms of overall and area-specific RMSE values. The challenge of conceptually delineating urban space is important. GUF, GHS, ESM and CopImp offer accurate information with regard to their respective objective. Beyond the classic mapping of densely built-up urban areas, machine learning regression might be particularly beneficial in areas with low built-up density and thus complementary to existing products. Using free Sentinel-2 data, SVR is potentially able to globally map settlements based on dense time series that also allow monitoring dynamic urban developments. Additionally, its ability to provide gradual land cover information is useful to better understand urban patterns. We therefore conclude that there is great potential of mapping a diversity of human settlements with Sentinel-2 when employing an SVR-based approach.