Focusing on a sustainable and strategic urban development, local governments and public administrations, such as the Veneto Region in Italy, are increasingly addressing their urban and territorial planning to meet national and European policies, along with the principles and goals of the 2030 Agenda for the Sustainable Development. In this regard, we aim at testing a methodology based on a semi-automatic approach able to extract the spatial extent of urban areas, referred to as “urban footprint”, from satellite data. In particular, we exploited Sentinel-1 radar imagery through multitemporal analysis of interferometric coherence as well as supervised and non-supervised classification algorithms. Lastly, we compared the results with the land cover map of the Veneto Region for accuracy assessments. Once properly processed and classified, the radar images resulted in high accuracy values, with an overall accuracy ranging between 85% and 90% and percentages of urban footprint differing by less than 1%–2% with respect to the values extracted from the reference land cover map. These results provide not only a reliable and useful support for strategic urban planning and monitoring, but also potentially identify a solid organizational dataflow process to prepare geographic indicators that will help answering the needs of the 2030 Agenda (in particular the goal 11 “Sustainable Cities and Communities”).