Influence of LiDAR Point Cloud Density in the Geometric Characterization of Rooftops for Solar Photovoltaic Studies in Cities

Abstract

The use of LiDAR (Light Detection and Ranging) data for the definition of the 3D geometry of roofs has been widely exploited in recent years for its posterior application in the field of solar energy. Point density in LiDAR data is an essential characteristic to be taken into account for the accurate estimation of roof geometry: area, orientation and slope. This paper presents a comparative study between LiDAR data of different point densities: 0.5, 1, 2 and 14 points/m2 for the measurement of the area of roofs of residential and industrial buildings. The data used for the study are the LiDAR data freely available by the Spanish Institute of Geography (IGN), which is offered according to the INSPIRE Directive. The results obtained show different behaviors for roofs with an area below and over 200 m2. While the use of low-density point clouds (0.5 point/m2) presents significant errors in the estimation of the area, the use of point clouds with higher density (1 or 2 points/m2) implies a great improvement in the area results, with no significant difference among them. The use of high-density point clouds (14 points/m2) also implies an improvement of the results, although the accuracy does not increase in the same ratio as the increase in density regarding 1 or 2 points/m2. Thus, the conclusion reached is that the geometrical characterization of roofs requires data acquisition with point density of 1 or 2 points/m2, and that higher point densities do not improve the results with the same intensity as they increase computation time.

Comparative analysis of sandy beach and foredune geomorphic change measurements from Apple LiDAR and small-unoccupied aerial systems

“…The Apple LiDAR point clouds are denser than those from sUAS SfM, with the scanner generating in total 83,179,489 points in July and 63,984,235 points in September, compared to the sUAS SfM point clouds which had 6,976,757 points in July and 7,179,555 points in September (Table 3). The higher density of the Apple LiDAR point clouds allows for a more detailed representation of the survey area [34]. This is particularly true in areas such as the foredunes where low elevation grasses occur on top of the bare sand, which is a documented challenge for rendering accurate bare earth DEMs using sUAS surveying methods.…”

Validation of a 3D Local-Scale Adaptive Solar Radiation Model by Using Pyranometer Measurements and a High-Resolution Digital Elevation Model

“…The National Geographic Institute (IGN) in Spain offers highly accurate and updated LiDAR data of the entire Spanish territory, captured periodically under the National Plan for Territory Observation (Plan Nacional de Observación del Territorio—PNOT) [ 38 ]. Previous studies have demonstrated the applicability of these LiDAR data in urban environments in Spain [ 39 , 40 , 41 , 42 ].…”

Remote Sensing for Monitoring Photovoltaic Solar Plants in Brazil Using Deep Semantic Segmentation

“…The present research had the following methodological steps (Figure 1 nighttime light satellite imagery [50][51][52], among others. In solar energy, many studies use remote sensing images, such as solar energy estimates [53][54][55][56], solar power plant site selection [57][58][59][60][61][62], PV potential on building rooftops [63][64][65][66], and area estimation [67,68].…”

“…Remote sensing data (aerial photography and satellite imagery) enable inspection periodically, and have been widely used in the electrical sector for effective maintenance of electrical lines [39][40][41], thermal monitoring from nuclear power plants [42][43][44][45], environmental changes from hydroelectric dams [46][47][48][49], and energy consumption using nighttime light satellite imagery [50][51][52], among others. In solar energy, many studies use remote sens-ing images, such as solar energy estimates [53][54][55][56], solar power plant site selection [57][58][59][60][61][62], PV potential on building rooftops [63][64][65][66], and area estimation [67,68].…”