## 2 CLARA WebGIS: Data Sources
### CLARA Data
#### 2.3.2 Digital Surface Model from Satellite Data  
In the framework of the CLARA project, we generated a DSM of the city of Matera and its surroundings, including the slope of the rocky ravine created by the Gravina stream [62]. In particular, the Agisoft Metahape photogrammetric software (Agisoft Metashape, 2021) [63] was used to process a cross-sensor multi-view satellite optical triplet composed of a WorldView-3 stereo pair and a GeoEye-1 image, whose acquisition features are described in [54]. Figure 5: **(A)** Map of neighborhoods and measured building locations; pie charts showing proportion (as percentage) of measured and total number of buildings in the related macro-area for (**B**) reinforced concrete and (**D**) masonry; proportion (as percentage) of the number of buildings for each area with respect to the total number of buildings in the studied urban area for (**C**) reinforced concrete and (**E**) masonry, according to census variables (ISTAT). First, three DSMs were produced using the three stereo pairs obtained by combining the three images. We followed the procedure described in [54], which adopts an original terrain-independent approach to refine the Rational Polynomial Coefficients (RPCs) supplied in each image metadata [64]. The geoid undulations derived from the EGM2008 model [65] were applied to transform the native ellipsoidal heights, derived from the RPC based orientation, to the corresponding orthometric ones. Moreover, we cropped the DSMs in accordance with the maximum intersection area common to the three DSMs, and we resampled our products to 0.5 m. Lastly, the final DSM was produced by computing a weighted mean of the three DSMs [54]. Moreover, an additional raster, containing the building heights with respect to the ground level for each pixel belonging to a building, was computed by subtracting the heights of the RSDI Digital Terrain Model (DTM)--resampled to the DSM resolution--from the DSM heights. To evaluate the accuracy of the overall DSM, our product was compared with a reference generated using the open data from RSDI. Specifically, we added the orthometric heights of the RSDI DTM, resampled to the DSM resolution, to the building eave heights of the shapefile called 'unita volumetrica' to produce a reference DSM. In this way, the height differences (\(\Delta\)Z) between the reference and the overall DSM were computed for each pixel (Figure 8), and the standard statistical indicators [66] such as mean, standard deviation, root mean square error (RMSE), median, normalized median absolute deviation (NMAD), Linear Error at 68% and 90% confidence interval (LE68 and LE90, respectively) were evaluated. To compute the indicators, a threshold \(\Delta\)Z equal to 20 m was adopted to remove the values outside the range (\(-\Delta\)Z, \(\Delta\)Z), which were considered outliers. Figure 6: (**A–O**) Pie charts showing proportions (as percentages) of major foundation soil geology for each macro-area. Every diagram reports the macro-area name, the number of buildings (n.build. ), the extension of the area in hectares (A(ha)), and the population density expressed as inhab/ha (pd). Figure 8: Error map (CRS: EPSG:32633-WGS 84/UTM, zone 33N-Projected) showing height differences (in m) between the reference and the obtained DSM. The black line represent a section detailed in Figure 10.  
Figure 7: (**A**) Pop-up window showing the factsheet of a sample measurement (HVNSR_69) made on a R.C. building; (**B**) attribute types; screenshots of downloadable (**C**) HVNSR text file and (**D**) HVNSR curve bmp file. The results were assessed using the entire DSM and considering the different land covers separately. To distinguish artificial surfaces as well as agricultural, forest, and semi-natural areas, we adopted the CORINE Land Cover inventory [67] at epoch 2012, resampled to the DSM resolution. Furthermore, the statistics over the area corresponding exclusively to the buildings were computed using the 'unita volumetrica' shapefile.