Monitoring with high resolution land cover and especially of urban areas is a key task that is more and more required in a number of applications (urban planning, health monitoring, ecology, etc.). At the moment, some operational products, such as the “Copernicus High Resolution Imperviousness Layer”, are available to assess this information, but the frequency of updates is still limited despite the fact that more and more very high resolution data are acquired. In particular, the recent launch of the Sentinel-2A satellite in June 2015 makes available data with a minimum spatial resolution of 10 m, 13 spectral bands, wide acquisition coverage and short time revisits, which opens a large scale of new applications. In this work, we propose to exploit the benefit of Sentinel-2 images to monitor urban areas and to update Copernicus Land services, in particular the High Resolution Layer imperviousness. The approach relies on independent image classification (using already available Landsat images and new Sentinel-2 images) that are fused using the Dempster–Shafer theory. Experiments are performed on two urban areas: a large European city, Prague, in the Czech Republic, and a mid-sized one, Rennes, in France. Results, validated with a Kappa index over 0.9, illustrate the great interest of Sentinel-2 in operational projects, such as Copernicus products, and since such an approach can be conducted on very large areas, such as the European or global scale. Though classification and data fusion are not new, our process is original in the way it optimally combines uncertainties issued from classifications to generate more confident and precise imperviousness maps. The choice of imperviousness comes from the fact that it is a typical application where research meets the needs of an operational production. Moreover, the methodology presented in this paper can be used in any other land cover classification task using regular acquisitions issued, for example, from Sentinel-2.