Automatic Parametrization and Shadow Analysis of Roofs in Urban Areas from ALS Point Clouds with Solar Energy Purposes

Abstract

A basic feature of modern and smart cities is their energetic sustainability, using clean and renewable energies and, therefore, reducing the carbon emissions, especially in large cities. Solar energy is one of the most important renewable energy sources, being more significant in sunny climate areas such as the South of Europe. However, the installation of solar panels should be carried out carefully, being necessary to collect information about building roofs, regarding its surface and orientation. This paper proposes a methodology aiming to automatically parametrize building roofs employing point cloud data from an Aerial Laser Scanner (ALS) source. This parametrization consists of extracting not only the area and orientation of the roofs in an urban environment, but also of studying the shading of the roofs, given a date and time of the day. This methodology has been validated using 3D point cloud data of the city of Santiago de Compostela (Spain), achieving roof area measurement errors in the range of ±3%, showing that even low-density ALS data can be useful in order to carry out further analysis with energetic perspective.

Automatic Parametrization of Urban Areas Using ALS Data: The Case Study of Santiago de Compostela

“…Once the point cloud has been preprocessed, the next step aims at classifying the two main groups of points that remain in the cloud: ground and roofs. This process has some similarities with regard to previous work [25]. First, a Delaunay Triangulation T Pp (a Triangulation T p is a N × 3 matrix representing sets of three point indices in the point cloud P that form a triangle whose circumcircle do not contain any other point) is computed on P p [31].…”

“…Therefore, this preprocessing stage consists of the application of a filter that removes points that do not belong to the ground or to the roofs. This filter is based on the normal vector of the points in P. For each point, a spherical neighborhood of 6 meters is computed (this neighborhood criteria has proven to be valid in previous work [25]), obtaining the coordinates of M neighbors, and Principal Component Analysis (PCA) is applied over the M × 3 covariance matrix defined by the coordinates of these M neighbors. The normal vector of the point is defined as the third eigenvector retrieved by PCA, which corresponds to the smallest eigenvalue [30].…”

“…First, a Delaunay Triangulation T Pp (a Triangulation T p is a N × 3 matrix representing sets of three point indices in the point cloud P that form a triangle whose circumcircle do not contain any other point) is computed on P p [31]. As vertical structures have been removed in the previous step, it is straightforward to find which triangles connect the ground and the roofs as those whose normal vector has an angle with respect to the vertical (that is, with respect to Z-axis) larger than a threshold β = 55 • (again, this threshold considers the possibility of inclined areas) and whose height difference between the highest and the lowest point is larger than h = 3 m. For every triangle that complies with these conditions, which were initially defined in [25], the highest and the lowest point are labeled as roof point and ground point, respectively (Figure 2c).…”

“…Starting from labeled points, a region growing algorithm assigns labels to every point in the cloud, taking into consideration the presence of vegetation or other artifacts. This process, which is explained in detail in [25], outputs two point clouds, P r containing the roofs, and P g containing the ground, such that P r ∩ P g = ∅ (Figure 2d).…”

“…These systems are able to collect data from large surfaces in much less time than a terrestrial system, but the spatial resolution of the resulting point clouds is considerably smaller. However, it is enough for a wide range of applications, such as roof extraction and parametrization [24,25] or urban vegetation assessment [26,27]. Of special interest for this manuscript is the usage of ALS technology for urban parametrization.…”