# A GeoNode-Based Platform for an Effective Exploitation of Advanced DInSAR Measurements
## 5 Results  
In order to demonstrate the effectiveness of the implemented extension of the GeoNode platform functionalities, we show in the following some examples relevant to the analysis of a DInSAR dataset of over all Italy. In particular, we focus on the DInSAR time-series relevant to the acquisitions of the Sentinel-1 constellation, collected during the 2015-2018 time interval from descending orbits. Note that this dataset has been generated through the SBAS-DInSAR processing approach and is composed by 17 frames [77; 78]. They have a maximum spatial extension of 56,000 km\({}^{2}\) and are relevant to a temporal sequence of Sentinel-1 images ranging from a minimum number of 152 to a maximum one of 181. Let us start our analysis by considering Figure 12, where we show a synoptic view of the mean deformation velocity map over Italy relevant to the considered 17 SAR frames, represented on the latitude-longitude grid. In addition, we show in Figure 13 some sample frames relevant to four selected zones composing the overall dataset of Figure 12. We remark that the DInSAR deformation results corresponding to each frame are included into txt files whose format has been shown in the previous Section (see Figure 5). Considering that for the investigated case study each txt file corresponding to the multi-temporal DInSAR deformation information relevant to a single frame has an average size of 1.5 GB, the overall dataset dimension is approximately equal to 26 GB; they correspond to a total of 17 million SAR pixels and for each of these a deformation time-series and the related products are available. Figure 11: Flow chart of the procedure for download data on a selected area. We highlight that the average time needed to load in the PostgreSQL/PostGIS geodatabase the text file relevant to a selected frame is approximately 10 min. With this regard it is worth underlining that the DInSAR dataset loading, as well as the overall analysis presented in the following, is based on a mid-range performance server, characterized by an AMD Quad-Core Opteron TMProcessor 2356 architecture, equipped with 32 GB of RAM and a 5 TB disk configur  
Figure 12: Sentinel-1 DInSAR mean deformation velocity map of whole Italian territory uploaded to the extended GeoNode platform. Figure 13: Mean deformation velocity maps relevant to a selection of four frames of the overall DInSAR dataset shown in Figure 12 including the: (**a**) Lombardia, Emilia-Romagna, Veneto and Trentino Alto Adige Regions, (**b**) Marche, Umbria, Lazio, Abruzzo, Molise and Campania Regions, (**c**) Lazio, Campania, Abruzzo and Molise Regions, and (**d**) Sicily and Calabria Regions. RAID5 with a storage extension of 11 TB. The Operating System (OS) used is the Ubuntu [114] distribution 14.04 LTS. The amount of disk storage for GeoWebCache and PostgreSQL/PostGIS geodatabase necessary for the creation of the interactive web map of Italy, is about 3 GB. We also remark that the exploited GeoWebCache architecture, as already discussed in Sections 3 and 4, allows us to speed up the simultaneous display of all areas, making the map an interactive web-based consultation tool, compatible with OGC standards. Moreover, the temporal and spatial deformation information stored in the geodatabase as tables, are effectively loaded on the map of GeoNode webGIS interface by using the strategy to exploit pre-build tiles of size 256 \(\times\) 256 pixels for each area and related zoom level (see Figure 8). Thanks to this strategy we may, for instance, very efficiently zoom in the different areas of the exploited DInSAR dataset, as for the case of the Campi Flegrei caldera (Southern Italy) shown in Figure 14.  
In this case we may clearly see the coherent SAR pixels, each of which is represented on the ground by a polygon with an area of about 90 \(\,\mathrm{m}\)\(\times\) 90 \(\,\mathrm{m}\), whose color is associated with its mean velocity value. Note also that the area extension of each polygon is consistent with the SRTM DEM [115] used to geocode the DInSAR results.