# A Complete and High-Resolution Estimate of Sardinia's Rootrop Photovoltaic Potential
## 1 Introduction
is a notable exception to other studies on the same geographic scale because of the level of detail in the analysis. Regional-scale analyses are typically carried out using statistical methods for extrapolating detailed analyses to samples. [PERSON] et al., for example, estimated the area available for PV installation using the clustering analysis of the urban and morphological characteristics of municipalities in Lombardy, the most densely populated region in Italy with the largest number of inhabitants [18]. Analyses on an urban scale, on the other hand, require the use of GIS data [20] or light detection and ranging (LiDAR) surveys [21; 22], or both [23; 24], and other data sources such as satellite images [25] or aerial photographic images [26] processed with machine-learning techniques [27; 28; 29]. In [20], for an area of the city of Turin (Italy) having an extension of 1 km\({}^{2}\) and containing 1228 buildings, GIS tools were used to estimate the tilt and orientation of rooftops from high-resolution (0.5 m) digital surface models. The problem of shading and effective irradiance had already been addressed in [23], where the sky view factor [30; 31], and obstructions due to terrain and buildings were calculated with LiDAR data for a small area in the city of Kingston, Ontario (Canada), and in [21], in which a three-dimensional model of buildings was developed to calculate shading and thus the actual irradiance on rooftops in the municipality of Avellino (Italy). More recently, in [22], a method was proposed for the automatic identification of roofs suitable for photovoltaic systems. The technique was demonstrated on buildings in the city of Gothenburg (Sweden) using a digital model of the surface with a resolution of 10 cm as the only input. The algorithm presented in [25] allowed for the examination of a 1 km\({}^{2}\) area at high resolution in the city of Giessen (Germany). In [24], a methodology was developed for the estimation of the rooftop potential for food, water, and photovoltaics in the city of Zaragoza (Spain), where LiDAR and cadastral data were used to identify suitable covers for the different purposes. Recently, techniques in machine learning have been added to the GIS methodologies mentioned above, such as in [26] where a fast method was proposed to three-dimensionally construct the geometry of buildings in an area of the Netherlands through the use of stereo aerial imagery with 10 cm resolution and cadastral information, while deep-learning techniques have found use for the segmentation of suitable roof surfaces in urban areas [27; 29]. Our work illustrates a complete methodology that allows for the high-resolution estimation of the photovoltaic potential at a regional scale. We applied the procedure on a large scale to an entire region of Italy, the island of Sardinia, and expanded the results presented at the city scale in [32]. Sardinia has great potential for generation from renewable sources and is included in the Coal Regions in Transition (CRiTs) initiative supported by the European Union [33]; therefore, it needs to develop and implement projects to realize a viable economic and technological transformation. As far as physical potential is concerned, we reworked the data provided by PVGIS [34]; in terms of the technical potential, and calculated the average energy production by means of a simplified formulation [35]; the economic potential was evaluated by means of an estimation of the levelized cost of electricity (LCOE) [36]. Our method focuses on the geographic potential and thus uses open cadastral, GIS, and LiDAR data to estimate the available rooftop surface, to approximate the inclination and orientation of building rooftops, and to evaluate the shading effects between buildings [32].