Nowadays, buildings are responsible of the 36% of CO2
emissions and 40% of the energy consumption in the European Union (EU), according to the European Commission. To improve the efficiency and sustainability of the energy consumed within buildings is important not only for reducing the carbon footprint but also for generating economic and social benefits related with the wellbeing of the building inhabitants and reducing the energy poverty. That is why the Energy Performance of Buildings Directive [1
] is aiming at nearly zero-energy standards, requiring all public buildings to satisfy this energetic efficiency by 2018 and all buildings by the end of 2020. Specifically, photovoltaic solar energy is widely used in urban environments, as it is a clean and silent source of energy, and it accounted for a 11.6% of the total quantity of electricity generated from renewable energy sources in the EU—that is, 28 countries in 2016 [2
]. Generally, there are three steps that are taken to estimate the solar potential: (1) Collection of input data (cartography, Light Detection and Ranging (LiDAR), or photogrammetry among others), (2) Development of a solar radiation model, and (3) Definition of an interface for the interaction with the end user [3
]. This work is entirely focused on the first step and its connection with the second, as identifying which areas are suitable for the use of solar energy is essential for the determination of the solar potential [4
], meaning that it is necessary to measure position, size, inclination and azimuth of the areas of installation of solar panels, which, in an urban environment, are typically the roofs of the buildings.
As it has been mentioned, there are different sources for the input data that can be used for energy applications. For instance, Nex and Ramondino [5
] generate DSM models from aerial images in order to reconstruct roof outlines. Similarly, Ahmadi et al. [6
] extract building boundaries also using imagery, being their research based on a model of active contours. The literature, however, has been more focused on employing data from ALS sources, which allow to collect accurate and dense 3D representations of the environment. Laser scanner data has been widely used in the last decade for a huge variety of applications. On one side, Terrestrial Laser Scanners (TLS) are typically employed for the detection and classification of objects at street level [7
], and for road and railway infrastructure analysis [10
]. Although TLS data is much denser than ALS and therefore the potential of this data source to capture small features with high resolution is higher, a terrestrial scan cannot collect geometric information about the building roofs as they will be always occluded. Therefore, Aerial data has to be employed, whose densities typically vary between 1–30 points per m2
to higher densities such as the ALS dataset presented in [13
], which averages 200 points per m2
by maximizing data coverage on building facades, flying at a low altitude and orientating flight paths at 45° with the major axes of the city streets, making possible a precise segmentation of building facades and roofs [14
]. Other applications of ALS data are the extraction of the road network centerlines [16
] or terrain recognition [18
]. Regarding the extraction of building roofs with ALS data, there also exist several related works that should be remarked. Yan et al. [20
] present a roof segmentation method based on a global plane fitting approach that achieves great accuracy results for point densities between 1.5 and 4 points per m2
. Vosselman et al. [21
] propose a point cloud classification framework that integrates a set of segmentation approaches based on segments and context, selecting features based on local analysis for the classification. With an energetic perspective, Lukač et al. [22
] present a photovoltaic estimation of building roofs, considering all the necessary parameters of a photovoltaic module and, on data collected with aerial LiDAR data. Finally, Lingfors et al. [23
] compare the performance of low-resolution and high-resolution airborne LiDAR data in order to automatically create a 2.5D building model of a neighborhood, while categorizing the buildings to perform a solar resource assessment.
The main contribution of this work is twofold: First, it presents a fully automatic methodology that segments ALS point clouds in order to extract building roofs and accurately measure several of their geometric features, all of them related with the determination of the solar potential. Furthermore, a shading analysis is proposed where the usable area of those roofs with the most suitable orientation for the installation of solar panels can be computed given any date and time of the day. The novelty of the work is also twofold: On one side, the methodological approach as a whole (although employing already existing techniques in some of its stages, such as triangulation). On the other side, this approach was developed with a focus on the case of the Spanish National Plan of Aerial Ortophotography, which provides aerial point clouds of the Spanish territory with known specifications.