## 2 Materials and Methods
### GIS Tools: Analysis of Building and Roof Typologies  
The analysis of roof typologies and the urban environment was carried out using several tools, explained below.  
* Slope tool was used to assess the roof slope of each building using the DSM and the municipal technical map. From the simulation results, the roofs were classified into three categories: (1) Flat roofs with a slope \(<\)11\({}^{\circ}\), identified as potential intensive green roofs; (2) pitched roofs with slope \(\geq\)11\({}^{\circ}\) and \(<\)20\({}^{\circ}\), as potential extensive green roofs; and (3) and pitched roofs with slope \(\geq\)20\({}^{\circ}\) and \(<\)45\({}^{\circ}\), as potential solar roofs [13].
* Aspect tool was used to assess the roof orientation using the DSM and the municipal technical map. Eight classes of roof surfaces' orientation were identified according to aspect values (that varied between 0\({}^{\circ}\) and 360\({}^{\circ}\)). Considering slope values and roof orientation, the pitched roofs were classified into five categories: Gable roofs with North-South (N-S) orientation, gable roofs with East-West (E-W) orientation, hipped/pyramid roofs, shed roof, and half-hipped roof [20].
* Feature Analyst tool was used to analyze roof materials with orthophotos as input data [21] to classify surfaces according to the color tones. In addition, from orthophotos the three bands (red, green, and blue) were analyzed with a GIS tool in order to optimize the classification, identifying dark/black, medium, and light/white roofs' colors.
* Area solar radiation tool was used to quantify the annual and monthly solar radiation values from the DSM. The quota of incident global solar radiation was quantified for each pixel (with a dimension of 0.5 m) and the hours of sunlight were calculated to identify sunny roofs (with three or more hours of sunlight) [22].
* Hillshade tool was used to create a shaded relief from the DSM by considering the illumination source angle and shadows, and in combination with other tools to evaluate roof-disturbing elements.  
* Zonal Statistics tool was able to calculate statistics' values of raster data for each roof surface. The roof-disturbing elements, such as dormers and antennas, were identified with the standard deviation using the orthophotos, the annual solar radiation analysis, and the hill-shade analysis. By overlapping the results of the statistical analysis, the disturbance percentage for each roof was assessed, identifying three classes of disturbance: 15, 25, and 35% [23]. Some outputs of the application of the described methodology are indicated in the following figures.  
Figure 2a shows the building typologies of a district of Turin with a dimension of 1 km \(\times\) 1 km. Such classification was made using information on type of users, building height, and the _S/V_ ratio values. It is possible to observe that almost 80% of buildings are residential, mainly linear blocks and towers [13]. Figure 2b describes the roof typologies, distinguishing six categories: flat, gable with E-W orientation, gable with N-S orientation, half-hipped, hipped or pyramid, and shed. In this pre-analysis, it was noticed that there is a potential of flat roofs that could be converted into green roofs (Figure 3a), the presence of low buildings with dark surfaces could be converted to light surfaces (high-reflectance roof), reducing the environmental temperature (Figure 2a), and a large quota of residential buildings has an optimal E-W orientation for solar energy production (Figure 3b).  
Figure 3: District of Turin with a dimension of 1 km\({}^{2}\): (**a**) Roof typology analysis and roof area; (**b**) building orientation analysis and roof area.  
Figure 2: District of Turin with a dimension of 1 km\({}^{2}\): (**a**) Identification of building typology using type of users, building height, and surface-to-volume (_S/V_) value; (**b**) identification of roof typologies according to [23].