## 4 Discussions
In Figure 8, we show an example of an EQtL that was detected in high detail by both ACD and CCD analyses conducted on Spotlight images, with less clear but still visible results obtained with StripMap data. Sentinel-1, on the other hand, was not able to detect this feature. Such information is fundamental for defining potential damage or interruptions to communication routes to avoid slowdowns in an emergency context and to guarantee the fastest and the most effective actions for rescue units. Lastly, Sentinel-1 has the advantage of providing background data collection with a 6-day revisit time, thus allowing very rapid temporal reactions (from 1 to 6 days); however, single-amplitude IW ground resolutions cannot aid in mapping or detecting damaged areas with the necessary precision. In contrast, recent studies that applied single Sentinel-1 images for natural or anthropic disaster mapping indicated that the images were useful when dealing with spatially widespread phenomena [31; 32]. However, if the aim is to detect and to map individual buildings or transportation routes, one must manage a time series of images or despeckled mean amplitudes [33]. In an emergency scenario, when only a single amplitude is available after the event, the S1 IW ground resolution is \(\sim 10\) m/s. Figure 8: Extracts of EQL in the northeastern sector of Norcia (red ellipses). (**A,B**) show the locations and pre/post-earthquake pictures of the landslide; (**C**) ACD Spotlight COSMO-SkyMed results; (**D**) CCD Spotlight COSMO-SkyMed results; (**E**) ACD StripMap COSMO-SkyMed descending results; and (**F**) ACD IW Sentinel-1 descending results. is not adequate to provide effective information to rescue units with the aim of properly addressing emergencies, identifying priorities, and quickly moving people to safe locations. On the other hand, the Sentinel-1 StripMap mode has a higher spatial resolution (5 m \(\times\) 5 m) than other modes, and it can be used to support emergency services; however, this imaging mode is not used regularly, and no archive data are available with global coverage [34]. In summary, the performances and the reaction times of the utilized datasets, as demonstrated, were functions of the combination of the: (i) ground resolution, (ii) revisit time, and (iii) availability of historical acquisition data in double-orbital geometry of each dataset; the results are recapped in Table 5.  
Considering an overall production effort of 6 h/10 km\({}^{2}\), we can derive that by using high resolution satellites it would be reliable to expect an accurate damage map provision up to 1.25 days after the event that can be a suitable time for evaluating the impact of the event and address rescues. In literature, at present, most of the other works investigate the potential of optical or radar images by exploiting images acquired even months after the event, however without considering timing, as we did, which plays a key role in natural disasters. We wanted to provide an operational contribution on the suitability of SAR data for the purposes of emergency management to direct the user responsible for the rescue to manage the emergency through a comparison of data with significantly different geometric resolution and revisit times. Most of these above-cited authors stated that the use of optical or ancillary data is needed to provide accurate damage maps. By using high-resolution and frequent revisit time SAR data, which at that time were not operational, we demonstrate that a quick-response damage map can be conducted in a \"blind\" manner without the use of ancillary or optical data. In fact, we used ancillary data (QuickTriage) just as ground truth to benchmark and to evaluate the performances of our approach. In addition, considerable literature applied sophisticated algorithms, such as machine learning, genetic algorithms, principal component analysis, etc. [6; 35; 36; 37], or multitemporal acquisitions, all methods that can involve a considerable computational effort and an emergency response timing not adequate.