Earth's surface deformation that occur as a consequence of an earthquake is a crucial information for investigating the causative source of the seismic event. In this context, the space-borne Differential Synthetic Aperture Radar Interferometry (DInSAR) has proven to be one of the key methods for the quantitative measurement of the Earth's surface deformation, with centimetres to millimetres accuracy . DInSAR relies on the evaluation of the phase difference between two SAR images, acquired from different orbital positions and at different times . Depending on the system configuration, the footprint of space-borne SAR acquisitions can span from a few kilometres up to hundreds of kilometres, making it particularly suitable for accurate investigations of wide areas at relative low cost. In these sense, according to USGS records , from 1992 to 2016, about 3700 earthquakes with significant magnitudes (Mw > 6.0) have occurred, while only a limited number of them has been successfully investigated through DInSAR . This is mainly due, apart the intrinsic limitation of the DInSAR technique, to the lack of a satellite program with a systematic and global acquisition policy, which are fundamental characteristics to allow creating DInSAR operational services at global scale. However, since the launch of the Copernicus Sentinel-1 SAR satellite missions in 2014 and 2016, the availability of SAR images dramatically increased. Indeed, this constellation acquires, with global coverage policy, radar images every 6/12 days over the same area, allowing us to dispose of a huge archive of SAR data that can be processed for obtaining co-seismic displacement maps in a short time frame and anywhere in the world. Considering the relevance of the satellite interferometric analysis for the hazards monitoring, as well as the availability of new radar systems as Sentinel-1, which are characterized by a high reliability level, is it therefore possible the development of operational services for the generation of DInSAR products, some of them being already in place [4, 5]. In this work an unsupervised and automatic tool for the generation of DInSAR co-seismic displacement maps is presented. Benefiting from the mostly global availability of Sentinel-1 SAR data and the on-line earthquake catalogues, the tool retrieves information about the depth and magnitude of recent earthquakes and triggers, if necessary, the interferometric process over the area affected by the seismic event. The workflow process is the following (Figure 1). First, the extraction of earthquake information (epicenter location, magnitude, time, ...) from the on-line public available web catalogues, as those provided by main international geophysical institutions (e.g. USGS , INGV ), is performed (Block A of Figure 1). The retrieved information is provided according to different standard formats (QuakeML, geoJSON, ...) and is accessible via subscription feeds that are updated with a defined frequency. The system is not limited to a single earthquake catalog interface. The relevant earthquake information is collected in accordance to an empirical magnitude and depth relation, which considers that only high magnitude (> Mw 6.0) and relatively shallow earthquakes (typically < 20 km) very likely induce a surface deformation that is detectable via DInSAR  (Block B). Among the earthquakes that respect the relation, only those with the epicentre on land (or even on water but that can likely induce detectable deformation on land) are processed. Once the occurred earthquake has been selected, the SAR data retrieval is performed via an automatic query to the open access Sentinel-1 catalogue (Block C). The query is performed over an area whose extension depends of the relation between magnitude, depth and epicenter location, which is derived from theoretical and empirical considerations and is susceptible of further tuning and refinement. Once all the tracks covering the earthquake area have been identified, the system retrieves all the available SAR Sentinel-1 data (from both ascending and descending passes) up to 30 days before the event (or at least 1 pre-event image even in a larger time span), in order to allow the generation of the co-seismic interferograms. The data retrieval, and accordingly the subsequent DInSAR processing, remains active up to 30 days after the event. Once the data are downloaded, they are processed through an efficient DInSAR algorithm  (Block D). According to this scenario and taking benefit from the operational capability of the Sentinel-1 constellation, the processing of the different tracks can be carried out in parallel, while actually their execution depends on the available computing resources and on the effective temporal acquisition of the SAR data. A processing prioritization of the different tracks on the basis of the post-event acquisition time has been implemented (according to a First come-First served policy). The tool provides wrapped interferograms and displacement maps (unwrapped interferograms converted in centimetres) in the satellite Line of Sight (LOS). The output data are provided according to the specification of the European Plate Observing System (EPOS)  research infrastructure, and will be made openly available through the EPOS portal, to be investigated and interpreted by the scientific community. The system has been implemented on in-house computing facilities and has been tested through a controlled experiment with several significant earthquakes. Although tested with Sentinel-1 data, the implemented tool is independent from the exploited SAR acquisitions, thus increasing the number of data to be processed. Indeed, the only dependency is on the catalog interface that, if does not respect an Open standard, requires the implementation of an appropriate wrapper. It is also worth noting that the presented tool, since it takes benefit from efficient and scalable DInSAR algorithms, can be exploited to perform large processing campaigns of all the co-seismic DInSAR pairs acquired by the Sentinel-1, and even ERS and ENVISAT, since their respective launch. To do this, disposing of proper computing facilities, such as those provided by the DIAS  platforms where data and processing are co-located, is strongly envisaged.